After Learning Evolutionary Retrofitting (AfterLearnER) consists in applying evolutionary optimization to refine fully trained machine learning models by optimizing a set of carefully chosen parameters or hyperparameters of the model, with respect to some actual, exact, and hence possibly non-differentiable error signal, performed on a subset of the standard validation set. The efficiency of AfterLearnER is demonstrated by tackling non-differentiable signals such as threshold-based criteria in depth sensing, the word error rate in speech resynthesis, the number of kills per life at Doom, computational accuracy or BLEU in code translation, image quality in 3D generative adversarial networks (GANs), and user feedback in image generation via latent diffusion models (LDM). This retrofitting can be done after training, or dynamically at inference time by taking into account the user feedback. The advantages of AfterLearnER are its versatility, the possibility to use non-differentiable feedback, including human evaluations (i.e., no gradient is needed), the limited overfitting supported by a theoretical study, and its anytime behavior. Last but not least, AfterLearnER requires only a small amount of feedback, i.e., a few dozen to a few hundred scalars, compared to the tens of thousands needed in most related published works.

The evolution principles underlying Evolutionary Algorithms can be applied in any search space (i.e., to any representation), provided we are able to define meaningful variation operators with respect to the problem at hand. From the historical bitstring, continuous variables and Finite State Automata to advanced program or structure embeddings and beyond, EC has gradually, and sometimes painfully, earned its spurs, turning from confidential pocketknife to recognized Swiss Army Knife. I will try to illustrate this historical perspective with various examples gathered during my 35 (omg!) years of research in EC, and to demonstrate how a thorough exploitation of the past can provide useful hints for an efficient exploration of the future.

Tokenization imposes a fixed granularity on the input text, freezing how a language model operates on data and how far in the future it predicts. Byte Pair Encoding (BPE) and similar schemes split text once, build a static vocabulary, and leave the model stuck with that choice. We relax this rigidity by introducing an autoregressive U-Net that learns to embed its own tokens as it trains. The network reads raw bytes, pools them into words, then pairs of words, then up to 4 words, giving it a multi-scale view of the sequence. At deeper stages, the model must predict further into the future -- anticipating the next few words rather than the next byte -- so deeper stages focus on broader semantic patterns while earlier stages handle fine details. When carefully tuning and controlling pretraining compute, shallow hierarchies tie strong BPE baselines, and deeper hierarchies have a promising trend. Because tokenization now lives inside the model, the same system can handle character-level tasks and carry knowledge across low-resource languages.

This paper introduces Interactive Latent Diffusion Model (IELDM), an encapsulation of a popular text-to-image diffusion model into an Evolutionary framework, allowing the users to steer the design of images toward their goals, alleviating the tedious trial-and-error process that such tools frequently require. The users can not only designate their favourite images, allowing the system to build a surrogate model based on their goals and move in the same directions, but also click on some specific parts of the images to either locally refine the image through dedicated mutation, or recombine images by choosing on each one some regions they like. Experiments validate the benefits of IELDM, especially in a situation where Latent Diffusion Model is challenged by complex input prompts.

The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targeting evolutionary symbolic regression. The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set. Repeated fusions within an individual gradually generate more complex expressions, ending up in what can be viewed as new features. These features are then linearly combined to best fit the training data. ZGP individuals then undergo specific crossover and mutation operators, and selection takes place between parents and offspring. ZGP is validated using a large number of public domain regression datasets, and compared to other symbolic regression algorithms, as well as to traditional machine learning algorithms. ZGP reaches state-of-the-art performance with respect to both types of algorithms, and demonstrates a low computational time compared to other symbolic regression approaches.

International audience

This paper introduces Deep Statistical Solvers (DSS), a new class of trainable solvers for optimization problems, arising e.g., from system simulations. The key idea is to learn a solver that generalizes to a given distribution of problem instances. This is achieved by directly using as loss the objective function of the problem, as opposed to most previous Machine Learning based approaches, which mimic the solutions attained by an existing solver. Though both types of approaches outperform classical solvers with respect to speed for a given accuracy, a distinctive advantage of DSS is that they can be trained without a training set of sample solutions. Focusing on use cases of systems of interacting and interchangeable entities (e.g. molecular dynamics, power systems, discretized PDEs), the proposed approach is instantiated within a class of Graph Neural Networks. Under sufficient conditions, we prove that the corresponding set of functions contains approximations to any arbitrary precision of the actual solution of the optimization problem. The proposed approach is experimentally validated on large linear problems, demonstrating super-generalisation properties; And on AC power grid simulations, on which the predictions of the trained model have a correlation higher than 99.99% with the outputs of the classical Newton-Raphson method (known for its accuracy), while being 2 to 3 orders of magnitude faster.

Evolution provides a creative fount of complex and subtle adaptations that often surprise the scientists who discover them. However, the creativity of evolution is not limited to the natural world: Artificial organisms evolving in computational environments have also elicited surprise and wonder from the researchers studying them. The process of evolution is an algorithmic process that transcends the substrate in which it occurs. Indeed, many researchers in the field of digital evolution can provide examples of how their evolving algorithms and organisms have creatively subverted their expectations or intentions, exposed unrecognized bugs in their code, produced unexpectedly adaptations, or engaged in behaviors and outcomes, uncannily convergent with ones found in nature. Such stories routinely reveal surprise and creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. Bugs are fixed, experiments are refocused, and one-off surprises are collapsed into a single data point. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This article is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

Evolutionary algorithms have been widely applied for solving dynamic constrained optimization problems (DCOPs) as a common area of research in evolutionary optimization. Current benchmarks proposed for testing these problems in the continuous spaces are either not scalable in problem dimension or the settings for the environmental changes are not flexible. Moreover, they mainly focus on non-linear environmental changes on the objective function. While the dynamism in some real-world problems exists in the constraints and can be emulated with linear constraint changes. The purpose of this paper is to introduce a framework which produces benchmarks in which a dynamic environment is created with simple changes in linear constraints (rotation and translation of constraint's hyperplane). Our proposed framework creates dynamic benchmarks that are flexible in terms of number of changes, dimension of the problem and can be applied to test any objective function. Different constraint handling techniques will then be used to compare with our benchmark. The results reveal that with these changes set, there was an observable effect on the performance of the constraint handling techniques.

The study of semantics in Genetic Programming (GP) has increased dramatically over the last years due to the fact that researchers tend to report a performance increase in GP when semantic diversity is promoted. However, the adoption of semantics in Evolutionary Multi-objective Optimisation (EMO), at large, and in Multi-objective GP (MOGP), in particular, has been very limited and this paper intends to fill this challenging research area. We propose a mechanism wherein a semantic-based distance is used instead of the widely known crowding distance and is also used as an objective to be optimised. To this end, we use two well-known EMO algorithms: NSGA-II and SPEA2. Results on highly unbalanced binary classification tasks indicate that the proposed approach produces more and better results than the rest of the three other approaches used in this work, including the canonical aforementioned EMO algorithms.

The AutoML approach aims to deliver peak performance from a machine learning portfolio on the dataset at hand. A Monte-Carlo Tree Search Algorithm Selection and Configuration (Mosaic) approach is presented to tackle this mixed (combinatorial and continuous) expensive optimization problem on the structured search space of ML pipelines. Extensive lesion studies are conducted to independently assess and compare: i) the optimization processes based on Bayesian Optimization or Monte Carlo Tree Search (MCTS); ii) its warm-start initialization based on meta-features or random runs; iii) the ensembling of the solutions gathered along the search. Mosaic is assessed on the OpenML 100 benchmark and the Scikit-learn portfolio, with statistically significant gains over AutoSkLearn, winner of all former AutoML challenges.

We address the problem of maintaining high voltage power transmission networks in security at all time. This requires that power flowing through all lines remain below a certain nominal thermal limit above which lines might melt, break or cause other damages. Current practices include enforcing the deterministic “N-1” reliability criterion, namely anticipating exceeding of thermal limit for any eventual single line disconnection (whatever its cause may be) by running a slow, but accurate, physical grid simulator. New conceptual frameworks are calling for a probabilistic risk based security criterion and are in need of new methods to assess the risk. To tackle this difficult assessment, we address in this paper the problem of rapidly ranking higher order contingencies including all pairs of line disconnections, to better prioritize simulations. We present a novel method based on neural networks, which ranks “N-1” and “N-2” contingencies in decreasing order of presumed severity. We demonstrate on a classical benchmark problem that the residual risk of contingencies decreases dramatically compared to considering solely all “N-1” cases, at no additional computational cost. We evaluate that our method scales up to power grids of the size of the French high voltage power grid (over 1000 power lines).

Per Instance Algorithm Configuration (PIAC) relies on features that describe problem instances. It builds an Empirical Performance Model (EPM) from a training set made of (instance, parameter configuration) pairs together with the corresponding performance of the algorithm at hand. This paper presents a case study in the continuous black-box optimization domain, using features proposed in the literature. The target algorithm is CMA-ES, and three of its hyper-parameters. Special care is taken to the computational cost of the features. The EPM is learned on the BBOB benchmark, but tested on independent test functions gathered from the optimization literature. The results demonstrate that the proposed approach can outperform the default setting of CMA-ES with as few as 30 or 50 time the problem dimension additional function evaluations for feature computation.

One of the principles of evolutionary multi-objective optimization is the conjoint optimization of the objective functions. However, in some cases, some of the objectives are easier to attain than others. This causes the population to lose diversity at a high rate and stagnate in early stages of the evolution. This paper presents the progressive addition of objectives (PAO) heuristic. PAO gradually adds objectives to a given problem relying on a perceived measure of complexity. This diversity loss phenomenon caused by the nature of a given objective has been observed when applying the Voronoi diagram-based evolutionary algorithm (VorEAl) in anomaly detection problems. Consequently, PAO has been first directed to address that issue. The experimental studies carried out show that the PAO heuristic manages to yield better results than the direct use of VorEAl on a group of test problems.

Semantic Backpropagation (SB) was introduced in GP so as to take into account the semantics of a GP tree at all intermediate states of the program execution, i.e., at each node of the tree. The idea is to compute the optimal "should-be" values each subtree should return, whilst assuming that the rest of the tree is unchanged, so as to minimize the fitness of the tree. To this end, the Random Desired Output (RDO) mutation operator, proposed in [17], uses SB in choosing, from a given library, a tree whose semantics are preferred to the semantics of a randomly selected subtree from the parent tree. Pushing this idea one step further, this paper introduces the Brando (BRANDO) operator, which selects from the parent tree the overall best subtree for applying RDO, using a small randomly drawn static library. Used within a simple Iterated Local Search framework, BRANDO can find the exact solution of many popular Boolean benchmarks in reasonable time whilst keeping solution trees small, thus paving the road for truly memetic GP algorithms.

This paper advocates a new ML-based programming framework, called Programming by Feedback (PF), which involves a sequence of interactions between the active computer and the user. The latter only provides preference judgments on pairs of solutions supplied by the active computer. The active computer involves two components: the learning component estimates the user’s utility function and accounts for the user’s (possibly limited) competence; the optimization component explores the search space and returns the most appropriate candidate solution. A proof of principle of the approach is proposed, showing that PF requires a handful of interactions in order to solve some discrete and continuous benchmark problems.

We investigate a method to deal with congestion of sectors and delays in the tactical phase of air traffic flow and capacity management. It relies on temporal objectives given for every point of the flight plans and shared among the controllers in order to create a collaborative environment. This would enhance the transition from the network view of the flow management to the local view of air traffic control. Uncertainty is modeled at the trajectory level with temporal information on the boundary points of the crossed sectors and then, we infer the probabilistic occupancy count. Therefore, we can model the accuracy of the trajectory prediction in the optimization process in order to fix some safety margins. On the one hand, more accurate is our prediction; more efficient will be the proposed solutions, because of the tighter safety margins. On the other hand, when uncertainty is not negligible, the proposed solutions will be more robust to disruptions. Furthermore, a multiobjective algorithm is used to find the tradeoff between the delays and congestion, which are antagonist in airspace with high traffic density. The flow management position can choose manually, or automatically with a preference-based algorithm, the adequate solution. This method is tested against two instances, one with 10 flights and 5 sectors and one with 300 flights and 16 sectors.

This paper presents a new mechanism for a better exploitation of surrogate models in the framework of Evolution Strategies (ESs). This mechanism is instantiated here on the self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (saACM-ES), a recently proposed surrogate-assisted variant of CMA-ES. As well as in the original saACM-ES, the expensive function is optimized by exploiting the surrogate model, whose hyper-parameters are also optimized online. The main novelty concerns a more intensive exploitation of the surrogate model by using much larger population sizes for its optimization.

This paper proposes a self-adaptation mechanism to manage the resources allocated to the different species comprising a cooperative coevolutionary algorithm. The proposed approach relies on a dynamic extension to the well-known multi-armed bandit framework. At each iteration, the dynamic multi-armed bandit makes a decision on which species to evolve for a generation, using the history of progress made by the different species to guide the decisions. We show experimentally, on a benchmark and a real-world problem, that evolving the different populations at different paces allows not only to identify solutions more rapidly, but also improves the capacity of cooperative coevolution to solve more complex problems.

This paper presents a novel mechanism to adapt surrogate-assisted population-based algorithms. This mechanism is applied to ACM-ES, a recently proposed surrogate-assisted variant of CMA-ES. The resulting algorithm, s*ACM-ES, adjusts online the lifelength of the current surrogate model (the number of CMA-ES generations before learning a new surrogate) and the surrogate hyper-parameters.

Parallel master-slave evolutionary algorithms easily lead to linear speedups in the case of a small number of nodes... and homogeneous computational costs of the evaluations. However, modern computer now routinely have several hundreds of nodes - and in many real-world applications in which fitness computation involves heavy numerical simulations, the computational costs of these simulations can greatly vary from one individual to the next. A simple answer to the latter problem is to use asynchronous steady-state reproduction schemes. But the resulting algorithms then differ from the original sequential version, with two consequences: First, the linear speedup does not hold any more; Second, the convergence might be hindered by the heterogeneity of the evaluation costs. The multi-objective optimization of a diesel engine is first presented, a real-world case study where evaluations are very heterogeneous in terms of CPU cost. Both the speedup of asynchronous parallel algorithms in case of large number of nodes, and their convergence toward the Pareto Front in case of heterogeneous computation times, are then experimentally analyzed on artificial test functions. An alternative selection scheme involving the computational cost of the fitness evaluation is then proposed, that counteracts the effects of heterogeneity on convergence toward the Pareto Front.

Benchmarking of optimization algorithms is necessary to quantitatively assess the performance of optimizers and to understand their strengths and weaknesses. The Black Box Optimization Benchmarking (BBOB) workshops that took place in 2009, 2010, and 2012 during the Genetic and Evolutionary Computation Conference (GECCO) were set up to benchmark both stochastic and deterministic continuous optimization algorithms. For this purpose, a thorough experimental setting, a set of test functions, and a visualization tool were designed and provided. They are based on the idea that (i) test functions should be representative of typical known difficulties, scalable with dimension, and not too easy to solve, yet comprehensible; and (ii) performance measures should be quantitative. A tool for acquiring and postprocessing data was provided.This special issue on Black Box Optimization Benchmarking contains papers that are extensions or based on results obtained during the BBOB GECCO 2009 and 2010 workshops. All articles were selected after the standard rigorous review process, from which seven papers in total were selected; five are published in this special issue and two papers will appear–because of space reasons–in a regular issue.We would like to thank all of the authors for contributing to the special issue as well as the reviewers for their reviews. We are indebted to Hans-Georg Beyer, Editor-in-Chief of Evolutionary Computation, for his patience and support. The works presented in this special issue rely heavily on the Comparing Continuous Optimizer (COCO) tool continuously developed since 2008 by the BBOB team among which we would like to thank in particular for their work and enthusiasm Raymond Ros, Steffen Finck, Petr Po.šì.k, Mike Preuss, Olaf Mersmann, and Verena Heidrich-Meisner.

Heuristic search algorithms have been successfully applied to solve many problems in practice. Their design, however, has increased in complexity as the number of parameters and choices for operators and algorithmic components is also expanding. There is clearly the need for providing the final user with automated tools to assist the tuning, design and assessment of heuristic optimisation methods. In recent years a growing number workshops and tracks has been held to address these issues. In 2010, the Parallel Problem Solving from Nature (PPSN) conference hosted two workshops, which decided to joint efforts to organise this journal special issue. The workshop “Self-Tuning, Self-Configuring and Self-Generating Search Heuristics,” distinguished three general processes in automated heuristic design: 1) tuning: the process of adjusting the algorithm's control parameters, 2) configuring: the process of selecting and using existing algorithmic components such as search operators, construction heuristics or acceptance criteria, and 3) generating: the process of creating altogether new heuristics (or heuristic components) from the basic sub-components of previously existing methods. Machine learning, meta-modelling and multilevel search approaches can and have been applied to automate these three processes. The workshop introduced the term ‘Self-* Search’, which is now the name of a track in GECCO, which started in 2011 and is also being held this year. The other workshop “Methods for the Assessment of Computational Systems” stressed the idea that the experimental analysis of computational systems inspired by nature can be made more sound and effective by the use of appropriate experimental methods. More severe requirements have been transmitted to draw objective conclusions from computational experiments, while at the same time the design and configuration of the computational systems can be improved by profitable ways of looking into the data collected.The quest for methods to automate the design and assessment of heuristic search methods is spawning a considerable amount of interdisciplinary research, mainly between the fields of computer science, artificial intelligence, optimization, statistics and machine learning. This special issue gathers contributions at the interface of these topics. It comprises five high quality papers that were selected after a rigorous reviewing process.The first two articles are related to the automatic, online configuration of heuristic search methods. Adaptive memetic algorithms (Ong et al., 2006) and selective hyper-heuristics (Burke et al., 2010) have developed separately. However, they share key research issues. In particular, they need to provide adaptive mechanisms to autonomously guide the choice of operators during the search. In the case of memetic algorithms, the choice is among a set of memes, which are generally local search heuristics. In the case of hyper-heuristics, the choice may involve different types of heuristics, such as constructive heuristics, mutational heuristics or neighborhood moves, crossovers and local search heuristics. Both algorithmic schemes require mechanisms for assigning rewards to operators according to their past performance and select which operator to apply at each decision point according to the computed qualities. These mechanisms have been also studied within the evolutionary computation community using the term Adaptive Operator Selection (Fialho et al., 2010).The first paper, “Estimating Meme Fitness in Adaptive Memetic Algorithms for Combinatorial Problems” by J. Smith studies two fundamental issues when assigning credit to search operators. First, whether it is better to assign credit to a meme based on an estimate of the extreme, or the mean benefit it causes. It has been found that, when the operator choice is related to mutation in a standard evolutionary algorithm, “extremal” versions that reward occasional large jumps rather than small steady improvements, produce better results. However, in the case of memes, which by design cause local improvement, the opposite was found in this study. The second issue concerns whether the aggregation of feedback from the search process should be global or local to some part of the solution space. Results suggest that local reward schemes outperform their global counterparts in combinatorial spaces, in contrast to continuous spaces. This study therefore confirms that the performance of credit assignment mechanisms depends on both the nature of the search space and the type of search operator.The paper “Hyper-Heuristics with Low Level Parameter Adaptation” by Z. Ren, H. Jiang, J. Xuan, and Z. Luo incorporates a search-based mechanism for adapting the parameters of the low-level heuristics in a hyper-heuristic framework. Traditionally, selective hyper-heuristics adaptively select the choice of fixed low-level heuristics. But clearly, some of these heuristics are parameterised (for example, the rate of a mutation operator). The proposed framework, then, simultaneously adapt the choice of low-level heuristics and their parameters, with improved results. It also proposes a mechanisms to separate the low-level heuristics into intensification and diversification heuristics, which helps to reduce the heuristic search space and improves efficiency.Parameter tuning of evolutionary algorithms is attracting more and more interest. In particular, the Sequential Parameter Optimization (SPO) is an established parameter tuning framework (Bartz-Beielstein et al., 2005). It uses the available budget (e.g., number of function evaluations) sequentially. Information from the exploration of the search space guides the search by building meta models. New design points are determined based on predictions from these meta models. The meta models are refined stepwise to improve knowledge about the search space. SPO provides techniques to cope with noise and guarantees comparable confidence for search points. It collects information to learn from this tuning process, e.g., integrated exploratory data analysis and provides mechanisms both for interactive and automated tuning. The following two papers discuss essential ways to improve SPO related algorithms by embedding transformations and resampling techniques. Their results are in no way restricted to parameter tuning or SPO.Since data from optimization runs are non-normal, transformations are tools of choice. The paper “On the Effect of Response Transformations in Sequential Parameter Optimization,” by T. Wagner and S. Wessing enhances the SPO framework by introducing transformation steps before the actual modeling. Based on design-of-experiments techniques, they analyze the effect of integrating different transformations. They demonstrate that in particular a rank transformation of the responses provides significant improvements. A deeper analysis of the resulting models and additional experiments with adaptive procedures indicate that the rank and the Box-Cox transformation are able to improve the properties of the result distributions with respect to symmetry and normality of the residuals.The paper “Resampling Methods for Meta-Model Validation, with Recommendations for Evolutionary Computation” by B. Bischl, O. Mersmann, H. Trautmann, and C. Weihs summarizes basic resampling methods from statistics, puts them into the context of meta-model validation and extensively discusses their advantages and disadvantages together with common pitfalls users shall avoid. Meta-model validation is then discussed as a supportive technique within evolutionary algorithms, also providing some concrete examples.Finally, the paper “An Experimental Approach to the Comparison of Continuous Metaheuristics Based on Landscape Topology” by R. Morgan and M. Gallagher extends previous work of the authors on Max-Set of Gaussians (MSG) problem generators. Two Estimation of Distribution type Evolutionary Algorithms (EDA) with different abilities to adapt to problem properties are compared on various randomly determined ridge landscapes, which are constructed by means of a modification of the MSG generator. The article also suggests two visualization tools that shall be helpful for the experimental analysis of non-deterministic optimization algorithms: heatmaps and parameterized difference plots. After detecting typical landscapes that favor either one or the other algorithm, the authors undertake a meta-search in the problem parameter space, maximizing the performance difference of the algorithms, thereby further enhancing the algorithm-problem interaction knowledge for this case.The guest editors wish to thank the contributing authors for their interesting submissions and the reviewers for their constructive feedback and detailed comments. We hope this special issue will promote the cross-fertilisation of ideas in assessing the performance and designing more autonomous and user-friendly heuristic search algorithms.

Master-slave parallelization of Evolutionary Algorithms (EAs) is straightforward, by distributing all fitness computations to slaves. The benefits of asynchronous steady state approaches are well-known when facing a possible heterogeneity among the evaluation costs in term of runtime, be they due to heterogeneous hardware or non-linear numerical simulations. However, when this heterogeneity depends on some characteristics of the individuals being evaluated, the search might be biased, and some regions of the search space poorly explored. Motivated by a real-world case study of multi-objective optimization problem the optimization of the combustion in a Diesel Engine the consequences of different components of heterogeneity in the evaluation costs on the convergence of two Evolutionary Multi-objective Optimization Algorithms are investigated on artificially-heterogeneous benchmark problems. In some cases, better spread of the population on the Pareto front seem to result from the interplay between the heterogeneity at hand and the evolutionary search.

Independence from the coordinate system is one source of efficiency and robustness for the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The recently proposed Adaptive Encoding (AE) procedure generalizes CMA-ES adaptive mechanism, and can be used together with any optimization algorithm. Adaptive Encoding gradually builds a transformation of the coordinate system such that the new coordinates are as decorrelated as possible with respect to the objective function. But any optimization algorithm can then be used together with Adaptive Encoding, and this paper proposes to use one of the simplest of all, that uses a dichotomy procedure on each coordinate in turn. The resulting algorithm, termed Adaptive Coordinate Descent (ACiD), is analyzed on the Sphere function, and experimentally validated on BBOB testbench where it is shown to outperform the standard (1+1)-CMA-ES, and is found comparable to other state-of-the-art CMA-ES variants.

Most works in multicellular artificial ontogeny solve the halting problem by arbitrarily limiting the number of iterations of the developmental process. Hence, the trajectory of the developing organism in the phenotypic space is only required to come close to an accurate solution during a very short developmental period. Because of the well-known opportunism of evolution, there is indeed no reason for the organism to remain close to a good solution in other situations: if the development is continued after the limiting bound; if the environment is perturbed by some noise during the development; if the development takes place in different physical conditions. In order to increase the robustness of the solution against such hazards, a new stopping criterion for the developmental process is proposed, based on the stability of some internal energy of the organism during its development. Such adaptive stopping criterion biases evolution toward solutions in which robustness is an intrinsic property. Experimental results on different “French flag” problems demonstrate that enforcing stable developmental process makes it possible to produce solutions that not only accurately approximate the target shape, but also demonstrate near-perfect self-healing properties, as well as excellent generalization capabilities.

Divide-and-Evolve (DaE) is an original "memeticization" of Evolutionary Computation and Artificial Intelligence Planning. However, like any Evolutionary Algorithm, DaE has several parameters that need to be tuned, and the already excellent experimental results demonstrated by DaE on benchmarks from the International Planning Competition, at the level of those of standard AI planners, have been obtained with parameters that had been tuned once and forall using the Racing method. This paper demonstrates that more specific parameter tuning (e.g. at the domain level or even at the instance level) can further improve DaE results, and discusses the trade-off between the gain in quality of the resulting plans and the overhead in terms of computational cost.

Adaptive Operator Selection (AOS) turns the impacts of the applications of variation operators into Operator Selection through a Credit Assignment mechanism. However, most Credit Assignment schemes make direct use of the fitness gain between parent and offspring. A first issue is that the Operator Selection technique that uses such kind of Credit Assignment is likely to be highly dependent on the a priori unknown bounds of the fitness function. Additionally, these bounds are likely to change along evolution, as fitness gains tend to get smaller as convergence occurs. Furthermore, and maybe more importantly, a fitness-based credit assignment forbid any invariance by monotonous transformation of the fitness, what is a usual source of robustness for comparison-based Evolutionary Algorithms. In this context, this paper proposes two new Credit Assignment mechanisms, one inspired by the Area Under the Curve paradigm, and the other close to the Sum of Ranks. Using fitness improvement as raw reward, and directly coupled to a Multi-Armed Bandit Operator Selection Rule, the resulting AOS obtain very good performances on both the OneMax problem and some artificial scenarios, while demonstrating their robustness with respect to hyper-parameter and fitness transformations. Furthermore, using fitness ranks as raw reward results in a fully comparison-based AOS with reasonable performances.

Most surrogate approaches to multi-objective optimization build a surrogate model for each objective. These surrogates can be used inside a classical Evolutionary Multiobjective Optimization Algorithm (EMOA) in lieu of the actual objectives, without modifying the underlying EMOA; or to filter out points that the models predict to be uninteresting. In contrast, the proposed approach aims at building a global surrogate model defined on the decision space and tightly characterizing the current Pareto set and the dominated region, in order to speed up the evolution progress toward the true Pareto set. This surrogate model is specified by combining a One-class Support Vector Machine (SVMs) to characterize the dominated points, and a Regression SVM to clamp the Pareto front on a single value. The resulting surrogate model is then used within state-of-the-art EMOAs to pre-screen the individuals generated by application of standard variation operators. Empirical validation on classical MOO benchmark problems shows a significant reduction of the number of evaluations of the actual objective functions.

The goal of adaptive operator selection is the on-line control of the choice of variation operators within evolutionary algorithms. The control process is based on two main components, the credit assignment, that defines the reward that will be used to evaluate the quality of an operator after it has been applied, and the operator selection mechanism, that selects one operator based on some operators qualities. Two previously developed adaptive operator selection methods are combined here: Compass evaluates the performance of operators by considering not only the fitness improvements from parent to offspring, but also the way they modify the diversity of the population, and their execution time; dynamic multi-armed bandit proposes a selection strategy based on the well-known UCB algorithm, achieving a compromise between exploitation and exploration, while nevertheless quickly adapting to changes. Tests with the proposed method, called ExCoDyMAB, are carried out using several hard instances of the satisfiability problem (SAT). Results show the good synergetic effect of combining both approaches.

Evolutionary Computation is an exciting research field with the power to assist researchers in the task of solving hard optimization problems (i.e., problems where the exploitable knowledge about the solution space is very hard and/or expensive to obtain). However, Evolutionary Algorithms are rarely used outside the circle of knowledgeable practitioners, and in that way have not achieved a status of useful enough tool to assist "general" researchers. We think that part of the blame is the lack of practical implementations of research efforts reflecting a unifying common ground in the field.

One of the choices that most affect the performance of Evolutionary Algorithms is the selection of the variation operators that are efficient to solve the problem at hand. This work presents an empirical analysis of different Adaptive Operator Selection (AOS) methods, i.e., techniques that automatically select the operator to be applied among the available ones, while searching for the solution. Four previously published operator selection rules are combined to four different credit assignment mechanisms. These 16 AOS combinations are analyzed and compared in the light of two well-known benchmark problems in Evolutionary Computation, the Royal Road and the Long K-Path.

The generation of network topologies with specific, user-specified statistical properties is addressed using an Evolutionary Algorithm that is seeded by an Artificial Gene Regulatory Network Model. The work presented here extends previous work where the proposed approach was demonstrated to be able to evolve scale-free topologies. The present results reinforce the applicability of the proposed method, showing that the evolution of small-world topologies is also possible, but requires a carefully crafted fitness function.

December 01 2009 Editorial Introduction In Special Collection: CogNet Marc Schoenauer Marc Schoenauer Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer Online Issn: 1530-9304 Print Issn: 1063-6560 © 2009 by the Massachusetts Institute of Technology2009 Evolutionary Computation (2009) 17 (4): i–ii. https://doi.org/10.1162/evco.2009.17.4.174i Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; Editorial Introduction. Evol Comput 2009; 17 (4): i–ii. doi: https://doi.org/10.1162/evco.2009.17.4.174i Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2009 by the Massachusetts Institute of Technology2009 Article PDF first page preview Close Modal You do not currently have access to this content.

A novel approach to generating scale-free network topologies is introduced, based on an existing artificial Gene Regulatory Network model. From this model, different interaction networks can be extracted, based on an activation threshold. By using an Evolutionary Computation approach, the model is allowed to evolve, in order to reach specific network statistical measures. The results obtained show that, when the model uses a duplication and divergence initialisation, such as seen in nature, the resulting regulation networks not only are closer in topology to scale-free networks, but also exhibit a much higher potential for evolution.

An important step toward self-tuning Evolutionary Algorithms is to design efficient Adaptive Operator Selection procedures. Such a procedure is made of two main components: a credit assignment mechanism, that computes a reward for each operator at hand based on some characteristics of the past offspring; and an adaptation rule, that modifies the selection mechanism based on the rewards of the different operators. This paper is concerned with the latter, and proposes a new approach for it based on the well-known Multi-Armed Bandit paradigm. However, because the basic Multi-Armed Bandit methods have been developed for static frameworks, a specific Dynamic Multi-Armed Bandit algorithm is proposed, that hybridizes an optimal Multi-Armed Bandit algorithm with the statistical Page-Hinkley test, which enforces the efficient detection of changes in time series. This original Operator Selection procedure is then compared to the state-of-the-art rules known as Probability Matching and Adaptive Pursuit on several artificial scenarios, after a careful sensitivity analysis of all methods. The Dynamic Multi-Armed Bandit method is found to outperform the other methods on a scenario from the literature, while on another scenario, the basic Multi-Armed Bandit performs best.

A possible alternative to fine topology tuning for Neural Network (NN) optimization is to use Echo State Networks (ESNs), recurrent NNs built upon a large reservoir of sparsely randomly connected neurons. The promises of ESNs have been fulfilled for supervised learning tasks, but unsupervised learning tasks, such as control problems, require more flexible optimization methods. We propose here to apply state-of-the-art methods in evolutionary continuous parameter optimization, to the evolutionary learning of ESN. First, a standard supervised learning problem is used to validate our approach and compare it to the standard quadratic one. The classical double pole balancing control problem is then used to demonstrate that unsupervised evolutionary learning of ESNs yields results that compete with the best topology-learning methods.

March 01 2008 Editorial Introduction In Special Collection: CogNet Marc Schoenauer Marc Schoenauer Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer Online Issn: 1530-9304 Print Issn: 1063-6560 © 2008 by the Massachusetts Institute of Technology2008 Evolutionary Computation (2008) 16 (1): iii. https://doi.org/10.1162/evco.2008.16.1.iii Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; Editorial Introduction. Evol Comput 2008; 16 (1): iii. doi: https://doi.org/10.1162/evco.2008.16.1.iii Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2008 by the Massachusetts Institute of Technology2008 Article PDF first page preview Close Modal You do not currently have access to this content.

This paper deals with the identification of the flux for a system of conservation laws in the specific example of analytic chromatography. The fundamental equations of chromatographic process are highly non linear. The state-of-the-art evolution strategy, CMA-ES (the covariance matrix adaptation evolution strategy), is used to identify the parameters of the so-called isotherm function. The approach was validated on different configurations of simulated data using either one, two or three components mixtures. CMA-ES is then applied to real data cases and its results are compared to those of a gradient-based strategy.

This paper introduces a continuous model for Multi-cellular Developmental Design. The cells are fixed on a 2D grid and exchange "chemicals" with their neighbors during the growth process. The quantity of chemicals that a cell produces, as well as the differentiation value of the cell in the phenotype, are controlled by a Neural Network (the genotype) that takes as inputs the chemicals produced by the neighboring cells at the previous time step. In the proposed model, the number of iterations of the growth process is not pre-determined, but emerges during evolution: only organisms for which the growth process stabilizes give a phenotype (the stable state), others are declared nonviable. The optimization of the controller is done using the NEAT algorithm, that optimizes both the topology and the weights of the Neural Networks. Though each cell only receives local information from its neighbors, the experimental results of the proposed approach on the 'flags' problems (the phenotype must match a given 2D pattern) are almost as good as those of a direct regression approach using the same model with global information. Moreover, the resulting multi-cellular organisms exhibit almost perfect self-healing characteristics.

This work introduces Autonomous selection in EAs to escape the need for some central control during the selection phases of an EA. The results demonstrate that this is a viable idea that needs further investigation.

Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers. Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Learning (EEL) approach presented in this paper features two contributions. First, a new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is used to extract the classifier ensemble from the final population only (Off-EEL) or incrementally along evolution (On-EEL). Experiments on a set of benchmark problems show that Off-EEL outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles.

June 01 2007 In Memoriam Laurence J. Fogel In Special Collection: CogNet Marc Schoenauer Marc Schoenauer May 2007 Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer May 2007 Online Issn: 1530-9304 Print Issn: 1063-6560 © 2007 by the Massachusetts Institute of Technology2007 Evolutionary Computation (2007) 15 (2): iii. https://doi.org/10.1162/evco.2007.15.2.iii Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; In Memoriam Laurence J. Fogel. Evol Comput 2007; 15 (2): iii. doi: https://doi.org/10.1162/evco.2007.15.2.iii Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2007 by the Massachusetts Institute of Technology2007 Article PDF first page preview Close Modal You do not currently have access to this content.

September 01 2007 Editorial Introduction In Special Collection: CogNet Marc Schoenauer Marc Schoenauer Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer Online Issn: 1530-9304 Print Issn: 1063-6560 © 2007 by the Massachusetts Institute of Technology2007 Evolutionary Computation (2007) 15 (3): iii. https://doi.org/10.1162/evco.2007.15.3.iii Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; Editorial Introduction. Evol Comput 2007; 15 (3): iii. doi: https://doi.org/10.1162/evco.2007.15.3.iii Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2007 by the Massachusetts Institute of Technology2007 Article PDF first page preview Close Modal You do not currently have access to this content.

The local reconstruction of a railway schedule following a small perturbation of the traffic, seeking minimization of the total accumulated delay, is a very difficult and tightly constrained combinatorial problem. Notoriously enough, the railway company's public image degrades proportionally to the amount of daily delays, and the same goes for its profit!.This paper describes an inoculation procedure which greatly enhances an evolutionary algorithm for train re-scheduling. The procedure consists in building the initial population around a pre-computed solution based on problem-related information available beforehand.The optimization is performed by adapting times of departure and arrival, as well as allocation of tracks, for each train at each station. This is achieved by a permutation-based evolutionary algorithm that relies on a semi-greedy heuristic scheduler to gradually reconstruct the schedule by inserting trains one after another.Experimental results are presented on various instances of a large real-world case involving around 500 trains and more than 1 million constraints. In terms of competition with commercial mathematical programming tool ILOG CPLEX, it appears that within a large class of instances, excluding trivial instances as well as too difficult ones, and with very few exceptions, a clever initialization turns an encouraging failure into a clear-cut success auguring of substantial financial savings.

March 01 2006 Editorial for the Special Issue on the Best of GECCO 2004 In Special Collection: CogNet Riccardo Poli, Riccardo Poli Search for other works by this author on: This Site Google Scholar Marc Schoenauer Marc Schoenauer December 2005 Search for other works by this author on: This Site Google Scholar Author and Article Information Riccardo Poli Marc Schoenauer December 2005 Online Issn: 1530-9304 Print Issn: 1063-6560 © 2006 Massachusetts Institute of Technology2006 Evolutionary Computation (2006) 14 (1): v. https://doi.org/10.1162/evco.2006.14.1.v Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Riccardo Poli, Marc Schoenauer; Editorial for the Special Issue on the Best of GECCO 2004. Evol Comput 2006; 14 (1): v. doi: https://doi.org/10.1162/evco.2006.14.1.v Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2006 Massachusetts Institute of Technology2006 Article PDF first page preview Close Modal You do not currently have access to this content.

March 01 2006 Editorial Introduction In Special Collection: CogNet Marc Schoenauer Marc Schoenauer January 2006 Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer January 2006 Online Issn: 1530-9304 Print Issn: 1063-6560 © 2006 Massachusetts Institute of Technology2006 Evolutionary Computation (2006) 14 (1): iii. https://doi.org/10.1162/evco.2006.14.1.iii Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; Editorial Introduction. Evol Comput 2006; 14 (1): iii. doi: https://doi.org/10.1162/evco.2006.14.1.iii Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2006 Massachusetts Institute of Technology2006 Article PDF first page preview Close Modal You do not currently have access to this content.

September 01 2006 Editorial Introduction In Special Collection: CogNet Marc Schoenauer Marc Schoenauer July 2006 Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer July 2006 Online Issn: 1530-9304 Print Issn: 1063-6560 © 2006 by the Massachusetts Institute of Technology2006 Evolutionary Computation (2006) 14 (3): iii. https://doi.org/10.1162/evco.2006.14.3.iii Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; Editorial Introduction. Evol Comput 2006; 14 (3): iii. doi: https://doi.org/10.1162/evco.2006.14.3.iii Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2006 by the Massachusetts Institute of Technology2006 Article PDF first page preview Close Modal You do not currently have access to this content.

December 01 2006 Editorial Introduction In Special Collection: CogNet Marc Schoenauer Marc Schoenauer October 2006 Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer October 2006 Online Issn: 1530-9304 Print Issn: 1063-6560 © 2006 by the Massachusetts Institute of Technology2006 Evolutionary Computation (2006) 14 (4): iii. https://doi.org/10.1162/evco.2006.14.4.iii Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; Editorial Introduction. Evol Comput 2006; 14 (4): iii. doi: https://doi.org/10.1162/evco.2006.14.4.iii Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2006 by the Massachusetts Institute of Technology2006 Article PDF first page preview Close Modal You do not currently have access to this content.

Train timetabling is a difficult and very tightly constrained combinatorial problem that deals with the construction of train schedules. We focus on the particular problem of local reconstruction of the schedule following a small perturbation, seeking minimisation of the total accumulated delay by adapting times of departure and arrival for each train and allocation of resources (tracks, routing nodes, etc.). We describe a permutation-based evolutionary algorithm that relies on a semi-greedy heuristic to gradually reconstruct the schedule by inserting trains one after another following the permutation. This algorithm can be hybridised with ILOG's commercial mixed integer programming (MIP) tool CPLEX in a coarse-grained manner: the evolutionary part is used to quickly obtain a good but suboptimal solution and this intermediate solution is refined using CPLEX. Experimental results are presented on a large real-world case involving more than 1 million variables and 2 million constraints. On this particular problem instance, results are surprisingly good in the early part of the search where the evolutionary algorithm reaches excellent, although suboptimal, solutions much faster than CPLEX alone. Over the whole search, although the hybridized version is less efficient on average, it does better and faster in a non negligible minority of cases.

A new representation for evolutionary subsurface identification from surface or well geological data is proposed. The idea is to represent the subsurface as the combination of, first, a geologically initial set of horizontal layers and, second, fault parameters like shape and displacements. Based on volume and bed-length preservation, a morphogenesis process then gives the structure at present time. A first implementation of this representation is tested on an artificial geological inverse problem in foothills region: the fault locations and dips are considered as two different objectives, and the e-MOEA multi-objective evolutionary algorithm is applied. The first results show the efficiency of the chosen representation.

A Quasi-Monte-Carlo method based on the computation of a surrogate model of the fitness function is proposed, and its convergence at super-linear rate 3/2 is proved under rather mild assumptions on the fitness function -- but assuming that the starting point lies within a small neighborhood of a global maximum. A memetic algorithm is then constructed, that performs both a random exploration of the search space and the exploitation of the best-so-far points using the previous surrogate local algorithm, coupled through selection. Under the same mild hypotheses, the global convergence of the memetic algorithm, at the same 3/2 rate, is proved.

Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of code bloat in the framework of symbolic regression in GP, from the viewpoint of Statistical Learning Theory, a well grounded mathematical toolbox for Machine Learning. Two kinds of bloat must be distinguished in that context, depending whether the target function lies in the search space or not. Then, important mathematical results are proved using classical results from Statistical Learning. Namely, the Vapnik-Chervonenkis dimension of programs is computed, and further results from Statistical Learning allow to prove that a parsimonious fitness ensures Universal Consistency (the solution minimizing the empirical error does converge to the best possible error when the number of examples goes to infinity). However, it is proved that the standard method consisting in choosing a maximal program size depending on the number of examples might still result in programs of infinitely increasing size with their accuracy; a more complicated modification of the fitness is proposed that theoretically avoids unnecessary bloat while nevertheless preserving the Universal Consistency.

A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. Among the most successful methods to automate the fuzzy controllers development process are evolutionary algorithms. In this work, we propose the Recurrent Fuzzy Voronoi (RFV) model, a representation for recurrent fuzzy systems. It is an extension of the FV model proposed by Kavka and Schoenauer that extends the application domain to include temporal problems. The FV model is a representation for fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, fulfilling the $\epsilon$-completeness property and providing a simple way to introduce a priory knowledge. In the proposed representation, the temporal relations are embedded by including internal units that provide feedback by connecting outputs to inputs. These internal units act as memory elements. In the RFV model, the semantic of the internal units can be specified together with the a priori rules. The geometric interpretation of the rules allows the use of geometric variational operators during the evolution. The representation and the algorithms are validated in two problems in the area of system identification and evolutionary robotics.

International audience

March 01 2005 Editorial Introduction In Special Collection: CogNet Marc Schoenauer Marc Schoenauer Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer Editor Online Issn: 1530-9304 Print Issn: 1063-6560 © 2005 Massachusetts Institute of Technology2005 Evolutionary Computation (2005) 13 (1): iii. https://doi.org/10.1162/1063656053583487 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; Editorial Introduction. Evol Comput 2005; 13 (1): iii. doi: https://doi.org/10.1162/1063656053583487 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2005 Massachusetts Institute of Technology2005 Article PDF first page preview Close Modal You do not currently have access to this content.

June 01 2005 Editorial Introduction In Special Collection: CogNet Marc Schoenauer Marc Schoenauer Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer Online Issn: 1530-9304 Print Issn: 1063-6560 © 2005 Massachusetts Institute of Technology2005 Evolutionary Computation (2005) 13 (2): iii. https://doi.org/10.1162/1063656054088521 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; Editorial Introduction. Evol Comput 2005; 13 (2): iii. doi: https://doi.org/10.1162/1063656054088521 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2005 Massachusetts Institute of Technology2005 Article PDF first page preview Close Modal You do not currently have access to this content.

September 01 2005 Editorial Introduction In Special Collection: CogNet Marc Schoenauer Marc Schoenauer Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer Online Issn: 1530-9304 Print Issn: 1063-6560 © 2005 Massachusetts Institute of Technology2005 Evolutionary Computation (2005) 13 (3): iii. https://doi.org/10.1162/1063656054794798 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; Editorial Introduction. Evol Comput 2005; 13 (3): iii. doi: https://doi.org/10.1162/1063656054794798 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2005 Massachusetts Institute of Technology2005 Article PDF first page preview Close Modal You do not currently have access to this content.

We present a multiobjective methodology, based on evolutionary computation, for solving the sensor planning problem for an active vision system. The application of different representation schemes, that allow to consider either fixed or variable size camera networks in a single evolutionary process, is studied. Furthermore, a novel representation of the recombination and mutation operators is brought forth. The developed methodology is incorporated into a 3D simulation environment and experimental results shown. Results validate the flexibility and effectiveness of our approach and offer new research alternatives in the field of sensor planning.

June 01 2004 Editorial Introduction In Special Collection: CogNet Marc Schoenauer Marc Schoenauer [email protected] Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer [email protected] Online Issn: 1530-9304 Print Issn: 1063-6560 © 2004 Massachusetts Institute of Technology2004 Evolutionary Computation (2004) 12 (2): iii. https://doi.org/10.1162/evco.2004.12.2.iii Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; Editorial Introduction. Evol Comput 2004; 12 (2): iii. doi: https://doi.org/10.1162/evco.2004.12.2.iii Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2004 Massachusetts Institute of Technology2004 Article PDF first page preview Close Modal You do not currently have access to this content.

September 01 2004 Editorial Introduction In Special Collection: CogNet Marc Schoenauer Marc Schoenauer [email protected] Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer [email protected] Online Issn: 1530-9304 Print Issn: 1063-6560 © 2004 Massachusetts Institute of Technology2004 Evolutionary Computation (2004) 12 (3): iii. https://doi.org/10.1162/1063656041774965 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; Editorial Introduction. Evol Comput 2004; 12 (3): iii. doi: https://doi.org/10.1162/1063656041774965 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2004 Massachusetts Institute of Technology2004 Article PDF first page preview Close Modal You do not currently have access to this content.

December 01 2004 Editorial Introduction In Special Collection: CogNet Marc Schoenauer Marc Schoenauer Search for other works by this author on: This Site Google Scholar Author and Article Information Marc Schoenauer Editor Online Issn: 1530-9304 Print Issn: 1063-6560 © 2004 Massachusetts Institute of Technology2004 Evolutionary Computation (2004) 12 (4): iii. https://doi.org/10.1162/1063656043138950 Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn MailTo Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Marc Schoenauer; Editorial Introduction. Evol Comput 2004; 12 (4): iii. doi: https://doi.org/10.1162/1063656043138950 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsEvolutionary Computation Search Advanced Search This content is only available as a PDF. © 2004 Massachusetts Institute of Technology2004 Article PDF first page preview Close Modal You do not currently have access to this content.

ASCHEA is an adaptive algorithm for constrained optimization problem based on a population level adaptive penalty function to handle constraints, a constraint-driven mate selection for recombination, and a segregational selection that favors a given number of feasible individuals. In this paper, we present some new results obtained using ASCHEA after extending the penalty function and introducing a niching technique with adaptive radius to handle multimodal functions. Furthermore, we propose a new equality constraint handling strategy. The idea is to start, for each equality, with a large feasible domain and to reduce it progressively along generations, in order to bring it as close as possible to null measure domain. Two approaches are proposed and experimented, the first based on dynamic adjustment and the second based on adaptive adjustment.

This paper presents a new air traffic route generator based on genetic algorithms. Due to traffic growth, direct (and near direct) routes are becoming increasingly congested and there is a real need for spreading traffic on new alternative routes. Those routes have to be different from several operational criteria and must not generate too much extra distance compared to the direct route. To reach this goal, a GA has been implemented with efficient sharing which automatically allows the emergence of different alternative routes. This algorithm has been tried on French air space and gives realistic operational results.

This paper describes a project funded by the European Commission which seeks to provide the technology and software infrastructure necessary to support the next generation of evolving infohabitants in a way that makes that infrastructure universal, open and scalable. The Distributed Resource Evolutionary Algorithm Machine (DREAM) will use existing hardware infrastructure in a more efficient manner, by utilising otherwise unused CPU time. It will allow infohabitants to co-operate, communicate, negotiate and trade; and emergent behaviour is expected to result. It is expected that there will be an emergent economy that results from the provision and use of CPU cycles by infohabitants and their owners. The DREAM infrastructure will be evaluated with new work on distributed data mining, distributed scheduling and the modelling of economic and social behaviour.

Recent work stresses the limitations of fitness distance correlation (FDC) as an indicator of landscape difficulty for genetic algorithms (GAs). Realizing that the fitness distance correlation (FDC) value cannot be reliably related to landscape difficulty, we investigate whether an interpretation of the whole correlation plot can yield reliable information about the behavior of the GA. Our approach is as follows. We present a generic method for constructing fitness functions which share the same fitness versus distance-to-optimum relation (FD relation). Special attention is given to FD relations which show no local optimum in the correlation plot, as is the case for the relation induced by Horn's longpath (J. Horn and D.E. Goldberg, 1995). We give an inventory of different types of GA behavior found within a class of fitness functions with a common correlation plot. We finally show that GA behavior can be very sensitive to small modifications of the fitness-distance relation.

In the past, the first way to reduce the congestion of the air traffic control system was to modify the structure of the airspace in order to increase the capacity (increasing the number of runways, increasing the number of sectors by reducing their size). This method has a limit due to the cost involved by new runways and the way to manage traffic in too small sectors (a controller needs a minimum amount of airspace to solve conflicts). The other way to reduce congestion is to modify the flight plans in order to adapt the demand to the available capacity. So, to reduce congestion, demand has to be spread in spatial and time dimension (route-slot allocation). Our research addresses the general time-route assignment problem using a static and a dynamic approach. A state of the art of the existing methods shows that this general bi-allocation problem is usually partially treated and the whole problem remains unsolved due to the induced complexity. GAs are then adapted to the problem.

Non-linear Iterated Function Systems (IFSs) are very powerful mathematical objects related to fractal theory, that can be used in order to generate (or model) very irregular shapes. We investigate how genetic programming techniques can be efficiently exploited in order to generate randomly or interactively artistic "fractal" 2D shapes. Two applications are presented for different types of nonlinear IFSs: interactive generation of mixed IFS attractors using a classical GP scheme; and random generation of Polar IFS attractors based on an "individual" approach of GP.

In the binary evolutionary optimization framework, two mutation operators are theoretically investigated. For both the standard mutation, in which all bits are flipped independently with the same probability, and the 1-bit-flip mutation, which flips exactly one bit per bitstring, the statistical distribution of the first hitting times of the target are thoroughly computed (expectation and variance) up to terms of order l (the size of the bitstrings) in two distinct situations: without any selection, or with the deterministic (1 + l)-ES selection on the OneMax problem. In both cases, the 1-bit-flip mutation convergence time is smaller by a constant (in terms of l) multiplicative factor. These results extend to the case of multiple independent optimizers.

A new evolution scheme is presented, memorizing the extreme (best and worst) past individuals through distributions over the binary search space. These distributions are used to bias the mutation operator in a (μ + λ) Evolution Strategy, guiding the

Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.