AI becomes increasingly important in safetycritical systems, especially in the case of autonomous systems, since navigation relies on AI for object detection and collision avoidance. However, safety-critical systems must adhere to functional safety standards that enforce software to be correct-by-construction, component decomposition to simplify design and validation, and the use of data only for testing purposes not to design the system itself. AI in general, and Deep Learning (DL) in particular have opposed characteristics since they have error rates (e.g., due to mispredictions), AI/DL modules can only be designed and validated monolithically, and they build on data for their design (i.e. for training purposes). Hence, DL solutions are at odds with the development process of safetycritical systems. A number of standards have recently emerged in different domains to reconcile the requirements of safety-critical systems with the characteristics of DL solutions, such as ISO 21448, ISO/IEC TR 5469, and ISO 8800, among others. However, there is a lack of realistic practice to design a DL-based safety-critical system in accordance with those regulations, and existing solutions only cover some aspects in isolation, and are often incompatible among them. SAFEXPLAIN is a 3-year Horizon Europe project addressing this challenge. SAFEXPLAIN, which finishes in September 2025, has already reached its main goals providing specific and complementary solutions to all those challenges so that AIbased safety-critical systems can be designed, implemented and validated adhering to the relevant functional safety standards in domains such as automotive, space and railway. In particular, SAFEXPLAIN provides the concepts, processes, tools and frameworks addressing the challenge end-to-end, from concept to solution. This is proven by the successful application of the SAFEXPLAIN approach in three case studies from the automotive, space and railway domains, whose results will see the light very soon.

We propose a systematic analysis of deep neural networks (DNNs) based on a signal processing technique for network parameter removal, in the form of synaptic filters that identifies the fragility, robustness and antifragility characteristics of DNN parameters. Our proposed analysis investigates if the DNN performance is impacted negatively, invariantly, or positively on both clean and adversarially perturbed test datasets when the DNN undergoes synaptic filtering. We define three filtering scores for quantifying the fragility, robustness and antifragility characteristics of DNN parameters based on the performances for (i) clean dataset, (ii) adversarial dataset, and (iii) the difference in performances of clean and adversarial datasets. We validate the proposed systematic analysis on ResNet-18, ResNet-50, SqueezeNet-v1.1 and ShuffleNet V2 x1.0 network architectures for MNIST, CIFAR10 and Tiny ImageNet datasets. The filtering scores, for a given network architecture, identify network parameters that are invariant in characteristics across different datasets over learning epochs. Vice-versa, for a given dataset, the filtering scores identify the parameters that are invariant in characteristics across different network architectures. We show that our synaptic filtering method improves the test accuracy of ResNet and ShuffleNet models on adversarial dataset when only the robust and antifragile parameters are selectively retrained at any given epoch, thus demonstrating applications of the proposed strategy in improving model robustness.

The paper proposes a novel adaptive search space decomposition method and a novel gradient-free optimization-based formulation for the pre- and post-buckling analyses of space truss structures. Space trusses are often employed in structural engineering to build large steel constructions, such as bridges and domes, whose structural response is characterized by large displacements. Therefore, these structures are vulnerable to progressive collapses due to local or global buckling effects, leading to sudden failures. The method proposed in this paper allows the analysis of the load-equilibrium path of truss structures to permanent and variable loading, including stable and unstable equilibrium stages and explicitly considering geometric nonlinearities. The goal of this work is to determine these equilibrium stages via optimization of the Lagrangian kinematic parameters of the system, determining the global equilibrium. However, this optimization problem is non-trivial due to the undefined parameter domain and the sensitivity and interaction among the Lagrangian parameters. Therefore, we propose to formulate this problem as a nonlinear, multimodal, unconstrained, continuous optimization problem and develop a novel adaptive search space decomposition method, which progressively and adaptively re-defines the search domain (hypersphere) to evaluate the equilibrium of the system using a gradient-free optimization algorithm. We tackle three benchmark problems and evaluate a medium-sized test representing a real structural problem in this paper. The results are compared to those available in the literature regarding displacement–load curves and deformed configurations. The accuracy and robustness of the adopted methodology show a high potential for gradient-free algorithms to analyze space truss structures.

We propose a novel algorithm called Backpropagation Neural Tree (BNeuralT), which is a stochastic computational dendritic tree. BNeuralT takes random repeated inputs through its leaves and imposes dendritic nonlinearities through its internal connections like a biological dendritic tree would do. Considering the dendritic-tree like plausible biological properties, BNeuralT is a single neuron neural tree model with its internal sub-trees resembling dendritic nonlinearities. BNeuralT algorithm produces an ad hoc neural tree which is trained using a stochastic gradient descent optimizer like gradient descent (GD), momentum GD, Nesterov accelerated GD, Adagrad, RMSprop, or Adam. BNeuralT training has two phases, each computed in a depth-first search manner: the forward pass computes neural tree's output in a post-order traversal, while the error backpropagation during the backward pass is performed recursively in a pre-order traversal. A BNeuralT model can be considered a minimal subset of a neural network (NN), meaning it is a "thinned" NN whose complexity is lower than an ordinary NN. Our algorithm produces high-performing and parsimonious models balancing the complexity with descriptive ability on a wide variety of machine learning problems: classification, regression, and pattern recognition.

The six papers in this special section focus on deep learning for genomics. Thanks to the development of high-throughput technologies, a huge amount of omics data is being produced relative to DNA and RNA sequences and (and also) abundance at individual subject or even at individual cell level. In particular, the genomics field is rich in data thanks to the rapid reduction in the cost of genetic sequencing. On the other hand, deep learning is transforming the field of many machine learning applications, such as computer vision and natural language processing, by effectively leveraging on big amount of data and is now emerging as a promising approach for many genomics modeling tasks. The scope of this special section is to discuss novel algorithms, methodologies and applications of deep learning to genomic studies with focus on their potentialities and challenges.

In this paper, we interpret Deep Neural Networks with Complex Network Theory. Complex Network Theory (CNT) represents Deep Neural Networks (DNNs) as directed weighted graphs to study them as dynamical systems. We efficiently adapt CNT measures to examine the evolution of the learning process of DNNs with different initializations and architectures: we introduce metrics for nodes/neurons and layers, namely Nodes Strength and Layers Fluctuation. Our framework distills trends in the learning dynamics and separates low from high accurate networks. We characterize populations of neural networks (ensemble analysis) and single instances (individual analysis). We tackle standard problems of image recognition, for which we show that specific learning dynamics are indistinguishable when analysed through the solely Link-Weights analysis. Further, Nodes Strength and Layers Fluctuations make unprecedented behaviours emerge: accurate networks, when compared to under-trained models, show substantially divergent distributions with the greater extremity of deviations. On top of this study, we provide an efficient implementation of the CNT metrics for both Convolutional and Fully Connected Networks, to fasten the research in this direction.

(DL)We present a novel approach to rank Deep Learning hyper-parameters through the application of Sensitivity Analysis (SA). DL hyper-parameter tuning is crucial to model accuracy however, choosing optimal values for each parameter is time and resource-intensive. SA provides a quantitative measure by which hyper-parameters can be ranked in terms of contribution to model accuracy. Learning rate decay was ranked highest, with model performance being sensitive to this parameter regardless of architecture or dataset. The influence of a model’s initial learning rate was proven to be low, contrary to the literature. Additionally, the importance of a parameter is closely linked to model architecture. Shallower models showed susceptibility to hyper-parameters affecting the stochasticity of the learning process whereas deeper models showed sensitivity to hyper-parameters affecting the convergence speed. Furthermore, the complexity of the dataset can affect the margin of separation between the sensitivity measures of the most and the least influential parameters, making the most influential hyper-parameter an ideal candidate for tuning compared to the other parameters.

We propose an algorithm and a new method to tackle the classification problems. We propose a multi-output neural tree (MONT) algorithm, which is an evolutionary learning algorithm trained by the non-dominated sorting genetic algorithm (NSGA)-III. Since evolutionary learning is stochastic, a hypothesis found in the form of MONT is unique for each run of evolutionary learning, i.e., each hypothesis (tree) generated bears distinct properties compared to any other hypothesis both in topological space and parameter-space. This leads to a challenging optimisation problem where the aim is to minimise the tree-size and maximise the classification accuracy. Therefore, the Pareto-optimality concerns were met by hypervolume indicator analysis. We used nine benchmark classification learning problems to evaluate the performance of the MONT. As a result of our experiments, we obtained MONTs which are able to tackle the classification problems with high accuracy. The performance of MONT emerged better over a set of problems tackled in this study compared with a set of well-known classifiers: multilayer perceptron, reduced-error pruning tree, naïve Bayes classifier, decision tree, and support vector machine. Moreover, the performances of three versions of MONT's training using genetic programming, NSGA-II, and NSGA-III suggests that the NSGA-III gives the best Pareto-optimal solution.

The fifteen articles in this special section focus on data science used in smart healthcare applications. A shift toward a data-driven socio-economic health model is occurring. This is the result of the increased volume, velocity and variety of data collected from the public and private sector in healthcare, and biology in general. In the past five-years, there has been an impressive development of computational intelligence and informatics methods for application to health and biomedical science. However, the effective use of data to address the scale and scope of human health problems has yet to realize its full potential. The barriers limiting the impact of practical application of standard data mining and machine learning methods have been inherent to the characteristics of health data. Besides the volume of the data (‘big data’), these are challenging due to their heterogeneity, complexity, variability and dynamic nature. Finally, data management and interpretability of the results have been limited by practical challenges in implementing new and also existing standards across the different health providers and research institutions. The scope of this Special issue is to discuss some of these challenges and opportunities in health and biological data science, with particular focus on the infrastructure, software, methods and algorithms needed to analyze large datasets in biological and clinical research.

We present a composite design methodology for the simulation and optimization of the solar cell performance. Our method is based on the synergy of different computational techniques and it is especially designed for the thin-film cell technology. In particular, we aim to efficiently simulate light trapping and plasmonic effects to enhance the light harvesting of the cell. The methodology is based on the sequential application of a hierarchy of approaches: (a) full Maxwell simulations are applied to derive the photon’s scattering probability in systems presenting textured interfaces; (b) calibrated Photonic Monte Carlo is used in junction with the scattering matrices method to evaluate coherent and scattered photon absorption in the full cell architectures; (c) the results of these advanced optical simulations are used as the pair generation terms in model implemented in an effective Technology Computer Aided Design tool for the derivation of the cell performance; (d) the models are investigated by qualitative and quantitative sensitivity analysis algorithms , to evaluate the importance of the design parameters considered on the models output and to get a first order descriptions of the objective space; (e) sensitivity analysis results are used to guide and simplify the optimization of the model achieved through both Single Objective Optimization (in order to fully maximize devices efficiency) and Multi Objective Optimization (in order to balance efficiency and cost); (f) Local, Global and “Glocal” robustness of optimal solutions found by the optimization algorithms are statistically evaluated; (g) data-based Identifiability Analysis is used to study the relationship between parameters. The results obtained show a noteworthy improvement with respect to the quantum efficiency of the reference cell demonstrating that the methodology presented is suitable for effective optimization of solar cell devices.

The protein structure refinement using conformational sampling is important in hitherto protein studies. In this paper, we examined the protein structure refinement by means of potential energy minimization using immune computing as a method of sampling conformations. The method was tested on the x-ray structure and 30 decoys of the mutant of [Leu]Enkephalin, a paradigmatic example of the biomolecular multiple-minima problem. In order to score the refined conformations, we used a standard potential energy function with the OPLSAA force field. The effectiveness of the search was assessed using a variety of methods. The robustness of sampling was checked by the energy yield function which measures quantitatively the number of the peptide decoys residing in an energetic funnel. Furthermore, the potential energy-dependent Pareto fronts were calculated to elucidate dissimilarities between peptide conformations and the native state as observed by x-ray crystallography. Our results showed that the probed potential energy landscape of [Leu]Enkephalin is self-similar on different metric scales and that the local potential energy minima of the peptide decoys are metastable, thus they can be refined to conformations whose potential energy is decreased by approximately 250 kJ/mol.

Artificial Life (hereafter ALife) is an interdisciplinary research field that has the purpose of gaining a better understanding of biological life by artificially synthesizing simple and novel forms of life, as well as reproducing lifelike properties of living systems. Synthesis of artificial cells, simulation of large-scale biological networks, intelligent use of exponentially growing amounts of biochemical data, exploitation of biological substrates for computation and control, and deployment of bio-inspired engineering are just some of the main issues of ALife and nowadays are cutting-edge topics.ALife is at the intersection between a theoretical perspective, namely, the scientific explanations of different levels of life organization (e.g., molecules, compartments, cells, tissues, organs, organisms, societies, and collective and social phenomena) and advanced technological applications (bio-inspired algorithms and techniques for building up effective solutions such as in the fields of robotics, big data analysis, and computational medicine).This special issue presents the top six articles—revised and extended—that were presented at ECAL 2013, the twelfth European Conference on Artificial Life, which was held in Taormina, Sicily, Italy, September 2–6, 2013 (http://www.dmi.unict.it/ecal2013/). ECAL 2013 was a grand scientific celebration with hundreds of paper and poster presentations (for a total of 267 submissions, 10 workshops, 16 tracks, and more than 350 attendants) by leading scientists in the field, which have described an impressive array of results, ideas, technologies, and applications, thus showing the current state of the art of ALife. The aim of this special issue is to give an incentive for boosting interest and new ideas in designing life and lifelike processes at different levels of complexity.After a careful peer-review process, only the top six research manuscripts, extended and revised, of ECAL 2013 have been selected for inclusion in this special issue.In “Multi-crease Self-folding by Global Heating,” by Shuhei Miyashita, Cagdas Onal, and Daniela Rus, the authors present a scheme for the autonomous folding of the body of a 3D robot from a 2D sheet using heat. They developed an easy, fast, and reliable fabrication technique for constructing a self-folding sheet. In particular, they exploit the thermal deformation of a constructive sheet sandwich made of rigid structural layers, and they utilize the V-fold method to attain the targeted folding angles. The authors also developed a mobile robot that uses such a self-folding body and that performs locomotion by using two vibration motors. The locomotion of the robot proves the functionality of the self-folding origami structure.In “The Search for Candidate Relevant Subsets of Variables in Complex Systems,” by M. Villani, A. Roli, A. Filisetti, M. Fiorucci, I. Poli, and R. Serra, the authors present a method to identify relevant subsets of variables for the comprehension of the organization of a dynamical system. Thus, the dynamical cluster index (DCI)—an information-theoretic measure—is introduced; the DCI relies on observations of current values of the relationships among the system variables, and does not require any previous knowledge. The usefulness of this method was tested in different application domains, where a known dynamical model generates the data, and the aim of the DCI is to uncover significant aspects of the organization of the model. One of the main novelties of this work is in the use of truly dynamical systems.In “Indirectly Encoding Running and Jumping Sodarace Creatures for Artificial Life,” by Paul Szerlip and Kenneth O. Stanley, the authors present a platform for evolving two-dimensional artificial creatures, whose aim is to serve for future artificial life experiments in evolving creatures. They introduce a new, indirectly encoded Sodarace (IESoR) system, which extends the original Sodarace by enabling the evolution of significantly more complex and regular creature morphologies. The capability of the system has been proved in both walking and jumping domains, in which IESoR discovered a wide breadth of strategies through the novel approach of search with local competition.In “Experiments on and Numerical Modeling of the Capture and Concentration of Transcription-Translation Machinery inside Vesicles,” by Fabio Mavelli and Pasquale Stano, the authors present a mathematical model of the encapsulation of transcription-translation (TX-TL) coupled reactions based on a minimal protein synthesis model and on different solute partition functions. Such a model can be used to predict the time span and the amount of protein produced starting from any pure system composition. The proposed approach highlights the role of stochastic events in synthetic-cell research, and emphasizes the importance of the integration of stochastic simulations with experimental approaches. The results presented show clearly that experimental data are compatible with an entrapment model, which follows a power law rather than a Gaussian distribution.In “Lessons from Speciation Dynamics: How to Generate Selective Pressure Towards Diversity,” by Heiko Hamann, the author detects how methods for generating selective pressure towards diversity (SPTD) can be transferred from the domain of artificial ecology (AE) to evolutionary robotics (ER). Furthermore, he also investigates how SPTD is generated without task-specific behavioral features or other forms of a priori knowledge. A promising finding of this work is that selective pressure towards unpopulated regions of the search space is generated for both systems. In particular, a beneficial finding is the analogy between the behavioral distances in ER and self-organizing ecology, in terms of the selective pressure generated.In “Cell-Division Behavior in a Heterogeneous Swarm Environment,” by Adam Erskine and J. Michael Herrmann, the authors present a system of virtual particles that interact using simple kinetic rules. In particular, they present a two-species three-dimensional swarm in which the behavior that emerges resembles cell division. In addition, they have proved that behavior resembling repeated cell division emerges from the low-level kinetic interactions of a heterogeneous swarm. Such division behavior exists in a small but finite volume of the swarm chemistry parameter space. Furthermore, from the outcomes it emerges how the behaviors differ depending on whether the swarm is moving in two- or three-dimensional space.The guest editors would like to thank all the authors of this special issue for their excellent work, constituting a valuable contribution to the state of the art and the scientific growth of ALife. We would also like to thank everyone who helped to ensure high scientific quality both of ECAL 2013 and of this special issue: the plenary speakers, Prof. Paolo Arena, Prof. Roberto Cingolani, Prof. Roberto Cipolla, Prof. Martin Hanczyc, Prof. Henrik Hautop Lund, Prof. Didier Keymeulen, Prof. Steve Oliver, Prof. Bernhard Ø. Palsson, and Prof. Rolf Pfeifer; all technical program committee members; the workshop chair; the tutorial chair; the publicity chair; and the local organizers. Special thanks go to the editor-in-chief of Artificial Life, Prof. Mark Bedau, for encouraging and accepting this special issue, and to the editorial assistant, Linda Reedijk, for her useful and important support in the realization of this volume.

We propose a framework to design metabolic pathways in which many objectives are optimized simultaneously. This allows to characterize the energy signature in models of algal and mitochondrial metabolism. The optimal design and assessment of the model is achieved through a multi-objective optimization technique driven by epsilon-dominance and identifiability analysis. A faster convergence process with robust candidate solutions is permitted by a relaxed Pareto dominance, regulating the granularity of the approximation of the Pareto front. Our framework is also suitable for black-box analysis, enabling to investigate and optimize any biological pathway modeled with ODEs, DAEs, FBA and GPR.

In low and high eukaryotes, energy is collected or transformed in compartments, the organelles. The rich variety of size, characteristics, and density of the organelles makes it difficult to build a general picture. In this paper, we make use of the Pareto-front analysis to investigate the optimization of energy metabolism in mitochondria and chloroplasts. Using the Pareto optimality principle, we compare models of organelle metabolism on the basis of single- and multiobjective optimization, approximation techniques (the Bayesian Automatic Relevance Determination), robustness, and pathway sensitivity analysis. Finally, we report the first analysis of the metabolic model for the hydrogenosome of Trichomonas vaginalis, which is found in several protozoan parasites. Our analysis has shown the importance of the Pareto optimality for such comparison and for insights into the evolution of the metabolism from cytoplasmic to organelle bound, involving a model order reduction. We report that Pareto fronts represent an asymptotic analysis useful to describe the metabolism of an organism aimed at maximizing concurrently two or more metabolite concentrations.

MOTIVATION: Metabolic engineering algorithms provide means to optimize a biological process leading to the improvement of a biotechnological interesting molecule. Therefore, it is important to understand how to act in a metabolic pathway in order to have the best results in terms of productions. In this work, we present a computational framework that searches for optimal and robust microbial strains that are able to produce target molecules. Our framework performs three tasks: it evaluates the parameter sensitivity of the microbial model, searches for the optimal genetic or fluxes design and finally calculates the robustness of the microbial strains. We are capable to combine the exploration of species, reactions, pathways and knockout parameter spaces with the Pareto-optimality principle. RESULTS: Our framework provides also theoretical and practical guidelines for design automation. The statistical cross comparison of our new optimization procedures, performed with respect to currently widely used algorithms for bacteria (e.g. Escherichia coli) over different multiple functions, reveals good performances over a variety of biotechnological products. AVAILABILITY: http://www.dmi.unict.it/nicosia/pathDesign.html. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. In fact, such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Indeed, having different objectives and different abstraction levels, most of these resources "speak" different languages. Semantic web technologies are here explored as a means to address some of these problems. METHODS: Employing an extensible collection of interpreters, we developed OREMP (Ontology Reasoning Engine for Molecular Pathways), a system that abstracts the information from different resources and combines them together into a coherent ontology. Continuing this effort we present OREMPdb; once different pathways are fed into OREMP, species are linked to the external ontologies referred and to reactions in which they participate. Exploiting these links, the system builds species-sets, which encapsulate species that operate together. Composing all of the reactions together, the system computes all of the reaction paths from-and-to all of the species-sets. RESULTS: OREMP has been applied to the curated branch of BioModels (2011/04/15 release) which overall contains 326 models, 9244 reactions, and 5636 species. OREMPdb is the semantic dictionary created as a result, which is made of 7360 species-sets. For each one of these sets, OREMPdb links the original pathway and the link to the original paper where this information first appeared.

In this paper we investigate plant photosynthesis and microbial fuel cells. We report the following: 1) we introduce and validate a novel multi-objective optimization algorithm, PMO2; 2) in photosynthesis we increase the yield of 135%, while in Geobacter sulfurreducens we determine the tradeoff for growth versus redox properties; 3) finally, we discuss Pareto-Front as an estimator of robust metabolic pathways.

We have studied the $\mathbf{C_3}$ photosynthetic carbon metabolism centering our investigation on the following four design principles. (1) Optimization of the photosynthetic rate by modifying the partitioning of resources between the different enzymes of the $\mathbf{C_3}$ photosynthetic carbon metabolism using a constant amount of protein-nitrogen. (2) Identify sensitive and less sensitive enzymes of the studied model. (3) Maximize photosynthetic productivity rate through the choice of robust enzyme concentrations using a new precise definition of robustness. (4) Modeling photosynthetic carbon metabolism as a multi-objective problem of two competing biological selection pressures: light-saturated photosynthetic rate versus total protein-nitrogen requirement. Using the designed single-objective optimization algorithms, PAO and A-CMA-ES, we have obtained an increase in photosynthetic productivity of the $\mathbf{135\%}$ from 15.486 $\mathbf{\mu mol~m^{-2}s^{-1}}$ to $\mathbf{36.382~\mu mol~m ^{-2}s^{-1}}$, and improving the previous best-found photosynthetic productivity value ($\mathbf{27.261}$ $\mathbf{\mu mol~m ^{-2}s^{-1}}$, $\mathbf{76\%}$ of enhancement). Optimized enzyme concentrations express a maximal local robustness ($\mathbf{100\%}$) and a high global robustness ($\mathbf{97.2\%}$), satisfactory properties for a possible ``in vitro'' manufacturing of the optimized pathway. Morris sensitivity analysis shows that 11 enzymes over 23 are high sensitive enzymes, i.e., the most influential enzymes of the carbon metabolism model. Finally, we have obtained the trade-off between the maximization of the leaf $\mathbf{CO_2}$ uptake rate and the minimization of the total protein-nitrogen concentration. This trade-off search has been carried out for the three $\mathbf{c_i}$ concentrations referring to the estimate of $\mathbf{CO_2}$ concentration in the atmosphere characteristic of 25 million years ago, nowadays and in 2100 a.C. Remarkably, the three Pareto frontiers identify the highest photosynthetic productivity rates together with the fewest protein-nitrogen usage.

The concept of information gain has been adopted as tool to study the effectiveness of population-based optimizers; using this approach, it is possible to infer convergence properties for population-based optimizers. The experimental results have shown characteristic phase transition between exploration and exploitation phase during the evolutionary process and, moreover, the evidence that gain maximization offers a robust theoretical framework to study the convergence of stochastic optimizers.

Designing semiconductor devices is a complex problem, which requires accurate models and effective optimizers. We have introduced a new methodology based on derivative-free optimization evolutionary algorithms, which aim to achieve a near-optimal design. We have tested these algorithms on various semiconductor devices using a drift-diffusion model; the experimental results show that CMA-ES and DE outperform the current state-of-the-art design algorithms.

A crucial task in designing semiconductor devices is to provide a doping profile that assures specific electrical properties. This research work is focused in redesigning doping profiles of semiconductor devices in order to obtain an increased output current; however, large doping levels can degenerate the devices and hence a trade-off between doping profile deviation and output current should be found. The doping profile optimization in semiconductor has been tackled as a multi-objective optimization problem using the Non-dominated Sorting Genetic Algorithm (NSGA-II). We focus on silicon diodes and MOSFET devices; firstly, we redesign the doping profile of diodes in order to obtain a trade-off between doping profile deviation and output current. Secondly, we find a trade-off between current and temperature for a MOSFET device. The experimental results confirm the effectiveness of the proposed approach to face this class of problems in electronic design automation.

The aim of this work is to compare some deterministic optimization algorithms and evolutionary algorithms on parameter estimation in a biological circuit design problem: the negative feedback loop between the tumor suppressor p53 and the oncogene Mdm2. We compared deterministic optimization algorithms and evolutionary algorithms in terms of robustness of the resulting parameters including all sources of uncertainty into the statistical representation of reference data and evaluating the obtained solutions in terms of confident limits. The experimental results obtained show as evolutionary algorithms are more robust with respect of deterministic optimization algorithms in particular the algorithm Differential Evolution (DE) showed the best performance over the minimization of the fitting function.

We present an immune algorithm (IA) inspired by the clonal selection principle, which has been designed for the protein structure prediction problem (PSP). The proposed IA employs two special mutation operators, hypermutation and hypermacromutation to allow effective searching, and an aging mechanism which is a new immune inspired operator that is devised to enforce diversity in the population during evolution. When cast as an optimization problem, the PSP can be seen as discovering a protein conformation with minimal energy. The proposed IA was tested on well-known PSP lattice models, the HP model in two-dimensional and three-dimensional square lattices', and the functional model protein, which is a more realistic biological model. Our experimental results demonstrate that the proposed IA is very competitive with the existing state-of-art algorithms for the PSP on lattice models

This work concerns the comparison of evolutionary algorithms and standard optimization methods on two circuit design problems: the parameter extraction of device circuit model and the multi-objective optimization of an operational transconductance amplifier. We compare standard optimization techniques and evolutionary algorithms in terms of quality of the solutions and computational effort, that is, objective function evaluations needed to compute them. The experimental results obtained show as standard techniques are robust with respect evolutionary algorithms, while the latter are more effective in terms of the standard metrics and function calls. In particular for the multiobjective problem, the observed Pareto front determined by evolutionary algorithms has a better spread of solutions with a larger number of nondominated solutions with respect to the standard multi-objective techniques.

This paper presents an immune algorithm (IA) based on clonal selection principle using a mutation operator, the hypermacromutation, and an aging process to tackle the protein structure prediction problem (PSP) in the 2D hydrophobic-hydrophilic model. The IA presented has only three parameters. To correctly set these parameters we compute the parameter surfaces, the 3D plots of IA success rate in function of the cloning parameter and the maximum age allowed to each B cell. The parameter surfaces show that hypermacromutation and aging operators are key features for generating diversity and searching more properly the funnel landscape of the PSP problem. Experiments show that the immune algorithm we propose is very competitive with the state-of-art algorithms for the PSP.