In most real-world settings, designs are often gradually adapted and improved over time. Consequently, there exists knowledge from distinct (but possibly related) design exercises, which have either been previously completed or are currently in-progress, that may be leveraged to enhance the optimization performance of a particular target optimization task of interest. Further, it is observed that modern day design cycles are typically distributed in nature, and consist of multiple teams working on associated ideas in tandem. In such environments, vast amounts of related information can become available at various stages of the search process corresponding to some ongoing target optimization exercise. Successfully exploiting this knowledge is expected to be of significant value in many practical settings, where solving an optimization problem from scratch may be exorbitantly costly or time consuming. Accordingly, in this paper, we propose an adaptive knowledge reuse framework for surrogate-assisted multiobjective optimization of computationally expensive problems, based on the novel idea of multiproblem surrogates. This idea provides the capability to acquire and spontaneously transfer learned models across problems, facilitating efficient global optimization. The efficacy of our proposition is demonstrated on a series of synthetic benchmark functions, as well as two practical case studies.

Heterogeneous domain adaptation needs supplementary information to link up different domains. However, such supplementary information may not always be available in real cases. In this paper, a new problem setting called hybrid domain adaptation is investigated. It is a special case of heterogeneous domain adaptation, in which different domains share some common features, but also have their own domain specific features. We leverage upon common features instead of supplementary information to achieve effective adaptation. We propose a general domain specific feature transfer framework, which can link up different domains using common features and simultaneously reduce domain divergences. Specifically, we learn the translations between common features and domain specific features. Then, we cross-use the learned translations to transfer the domain specific features of one domain to another domain. Finally, we compose a homogeneous space in which the domain divergences are minimized. We instantiate the general framework to a linear case and a nonlinear case. Extensive experiments verify the effectiveness of the two cases.

Marginalized stacked denoising autoencoder (mSDA), has recently emerged with demonstrated effectiveness in domain adaptation. In this paper, we investigate the rationale for why mSDA benefits domain adaptation tasks from the perspective of adaptive regularization. Our investigations focus on two types of feature corruption noise: Gaussian noise (mSDAg) and Bernoulli dropout noise (mSDAbd). Both theoretical and empirical results demonstrate that mSDAbd successfully boosts the adaptation performance but mSDAgfails to do so. We then propose a new mSDA with data-dependent multinomial dropout noise (mSDAmd) that overcomes the limitations of mSDAbdand further improves the adaptation performance. Our mSDAmdis based on a more realistic assumption: different features are correlated and, thus, should be corrupted with different probabilities. Experimental results demonstrate the superiority of mSDAmdto mSDAbdon the adaptation performance and the convergence speed. Finally, we propose a deep transferable feature coding (DTFC) framework for unsupervised domain adaptation. The motivation of DTFC is that mSDA fails to consider the distribution discrepancy across different domains in the feature learning process. We introduce a new element to mSDA: domain divergence minimization by maximum mean discrepancy. This element is essential for domain adaptation as it ensures the extracted deep features to have a small distribution discrepancy. The effectiveness of DTFC is verified by extensive experiments on three benchmark data sets for both Bernoulli dropout noise and multinomial dropout noise.

Ordinal regression is a supervised learning problem aiming to classify instances into ordinal categories. It is challenging to automatically extract high-level features for representing intraclass information and interclass ordinal relationship simultaneously. This paper proposes a constrained optimization formulation for the ordinal regression problem which minimizes the negative loglikelihood for multiple categories constrained by the order relationship between instances. Mathematically, it is equivalent to an unconstrained formulation with a pairwise regularizer. An implementation based on the CNN framework is proposed to solve the problem such that high-level features can be extracted automatically, and the optimal solution can be learned through the traditional back-propagation method. The proposed pairwise constraints make the algorithm work even on small datasets, and a proposed efficient implementation make it be scalable for large datasets. Experimental results on four real-world benchmarks demonstrate that the proposed algorithm outperforms the traditional deep learning approaches and other state-of-the-art approaches based on hand-crafted features.

For the purpose of monitoring the health conditions of electrical machines, a framework is proposed to establish the methods to provide an early warning to potential machine failures in data mining terminology. The framework consists of five stages including data segmentation, feature extraction/selection, multi-classifier ensemble, decision fusion and output, which is flexible and can be adapted for any known faults. The difference lies in the implementation choices of techniques and structures (e.g. number of classifiers) in the second to forth stage as well as the input requirements. As an example, the turn-to-turn short circuit fault of induction motor is used as the known fault in studies in this work. Simulation results show the effectiveness of the proposed techniques.

In our turbine performance assessment, we need to provide an effective visual analytics tool in handling high-dimensional datasets. We have employed RadViz in 2D exploratory data analysis. However, with the increase of dataset size and dimensionality, the clumping of projected data points towards the origin in RadViz causes low space utilization, which largely degenerates the visibility of the feature characteristics. In this study, to better evaluate the hidden patterns in the center region, we propose histogram-based techniques to manipulate the radial distribution of data points in RadViz. We present RadViz in the polar coordinate system for convenient radial operations. Based on this, we define the radial equalization method to automatically spread out the frequency and the radial specification method to shape the distribution based on the user's requirement. Furthermore, we utilize the information in high-dimensional space as histogram and reference point to design and control the radial distribution of RadViz. Computational experiments have been conducted on turbine performance simulation data. Our proposed techniques are shown advantageous in query result display and outlier detection with a set of high-dimensional datasets.

Heterogeneous domain adaptation needs supplementary information to link up domains. However, this supplementary information is unavailable in many real cases. In this paper, a new problem setting called hybrid domain adaptation is investigated. It is a special case of heterogeneous domain adaptation in which different domains share some common features, but also have their own domain specific features. In this case, it can be efficiently solved without any supplementary information by using the common features to link up the domains in adaptation. We propose a domain specific feature transfer (DSFT) method, which can link up different domains using the common features and simultaneously reduce domain divergences. Specifically, we first learn the translations between the common features and the domain specific features. Then we cross-use the learned translations to transfer the domain specific features of one domain to another domain. Finally, we compose a homogeneous space in which the domain divergences are minimized. Extensive experiments verify the effectiveness of our proposed method.

A key challenge in multi-source transfer learning is to capture the diverse inter-domain similarities. In this paper, we study different approaches based on Gaussian process models to solve the multi-source transfer regression problem. Precisely, we first investigate the feasibility and performance of a family of transfer covariance functions that represent the pairwise similarity of each source and the target domain. We theoretically show that using such a transfer covariance function for general Gaussian process modelling can only capture the same similarity coefficient for all the sources, and thus may result in unsatisfactory transfer performance. This leads us to propose TC$_{MS}$Stack, an integrated strategy incorporating the benefits of the transfer covariance function and stacking. Extensive experiments on one synthetic and two real-world datasets, with learning settings of up to 11 sources for the latter, demonstrate the effectiveness of our proposed TC$_{MS}$Stack.

Ordinal regression aims to classify instances into ordinal categories. As with other supervised learning problems, learning an effective deep ordinal model from a small dataset is challenging. This paper proposes a new approach which transforms the ordinal regression problem to binary classification problems and uses triplets with instances from different categories to train deep neural networks such that high-level features describing their ordinal relationship can be extracted automatically. In the testing phase, triplets are formed by a testing instance and other instances with known ranks. A decoder is designed to estimate the rank of the testing instance based on the outputs of the network. Because of the data argumentation by permutation, deep learning can work for ordinal regression even on small datasets. Experimental results on the historical color image benchmark and MSRA image search datasets demonstrate that the proposed algorithm outperforms the traditional deep learning approach and is comparable with other state-of-the-art methods, which are highly based on prior knowledge to design effective features.

Class imbalance problems, where the number of samples in each class is unequal, is prevalent in numerous real world machine learning applications. Traditional methods which are biased toward the majority class are ineffective due to the relative severity of misclassifying rare events. This paper proposes a novel evolutionary cluster-based oversampling ensemble framework, which combines a novel cluster-based synthetic data generation method with an evolutionary algorithm (EA) to create an ensemble. The proposed synthetic data generation method is based on contemporary ideas of identifying oversampling regions using clusters. The novel use of EA serves a twofold purpose of optimizing the parameters of the data generation method while generating diverse examples leveraging on the characteristics of EAs, reducing overall computational cost. The proposed method is evaluated on a set of 40 imbalance datasets obtained from the University of California, Irvine, database, and outperforms current state-of-the-art ensemble algorithms tackling class imbalance problems.

For accurate prognostics, users have to determine the current health of the system and predict future degradation pattern of the system. An increasingly popular approach toward tackling prognostic problems involves the use of switching models to represent various degradation phases, which the system undergoes. Such approaches have the advantage of determining the exact degradation phase of the system and being able to handle nonlinear degradation models through piecewise linear approximation. However, limitations of such existing methods include, limited applicability due to the discretization of predicted remaining useful life, insufficient robustness due to the use of single models and others. This paper circumvents these limitations by proposing a hybrid of ensemble methods with switching methods. The proposed method first implements a switching Kalman filter (SKF) to classify between various linear degradation phases, then predict the future propagation of fault dimension using appropriate Kalman filters for each phase. This proposed method achieves both continuous and discrete prediction values representing the remaining life and degradation phase of the system, respectively. The proposed framework is shown via a case study on benchmark simulated aeroengine data sets. The evaluation of the proposed framework shows that the proposed method achieves better accuracy and robustness against noise compared with other methods reported in the literature. The results also indicate the effectiveness of the SKF in detecting the switching point between various degradation modes.

Compression Resin Transfer Moulding is a popular method for high volume production of superior quality fibre-reinforced polymer composite parts. However, the process involves a large number of design variables that must be carefully chosen in order to reduce cycle time, capital layout and running costs, while maximizing final part quality. These objectives are principally governed by two separate phases of the manufacturing cycle, namely the resin filling and curing phases. It turns out that independently optimizing either phase (which is the general practice) may often lead to conditions that significantly restrict or even adversely affect the progress of the other. In light of this fact, a novel approach of modelling the entire composites manufacturing problem as bi-level program, one that assimilates both phases, has been adopted in this paper. In particular, an efficient multi-objective bi-level evolutionary algorithm is designed to effectively deal with the computationally expensive simulation-based optimization problem. The unique feature of the algorithm is that it incorporates a Pareto Rank Learning scheme, together with surrogate assistance for the upper level problem, in order to eliminate several expensive but redundant objective function evaluations. The optimization process is therefore considerably accelerated, assisting manufacturers in making improved decisions for this complex engineering design problem.

The anomaly detection task plays an important role in quality control in many industrial or manufacturing processes. However, in many such processes, anomaly detection is done visually by human experts who have in-depth knowledge and vast experience on a product in order to perform well in the detection task. In this paper, we present an approach that (i) identifies anomalies in an image based on the sparse residuals (or errors) obtained during image reconstruction using sparse representation and (ii) learns the threshold to classify an image pixel based on its residual value. The intuitions for our proposed sparse approximation driven approach are, namely: (i) anomalies are infrequent and (ii) anomalies are unwanted portions of an image reconstruction. Empirical results on a real-world image dataset for an industrial surface defect detection task are used to demonstrate the feasibility of our proposed approach.

This paper develops a framework for determining the Remaining Useful Life (RUL) of aero-engines. The framework includes the following modular components: creating a moving time window, a suitable feature extraction method and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on the publicly available C-MAPSS dataset. The prognostic accuracy of the proposed algorithm is also compared against other state-of-the-art methods available in the literature and it has been shown that the proposed framework has the best overall performance.

As scientific applications like Computational Fluid Dynamics (CFD) simulations generate more and more data, co-processing becomes the most cost effective way to process the vast amount of data generated by these simulation. In a co-processing environment, analysis and/or visualization of intermediate results occur concurrently to the simulation itself. Improved efficiency and early insight into the simulation process and results are potential advantages in comparison to postprocessing, where analysis and/or visualization are performed after the completion of the simulation. To enable co-processing, however, intermediate data needs to be shared between simulation and data analysis, and some degree of coordination may be required to maintain the correctness of both simulation and data analysis. The overhead incurred to facilitate data sharing and coordination may well offset benefits gained, particularly where distributed, large-scale systems are involved as workload sharing, processor affinity and data locality introduce significant effects to the overall performance. In this paper, we propose a co-processing framework to address these issues. The empirical benchmarking results suggest that co-processing overhead tasks scale well with the system size, the overall gain of about 20% in turnaround time compared to post-processing and that the coprocessing framework allows simulation and data analysis task to scale up to their individual limits.

Stochastic optimization of computationally expensive problems is a relatively new field of research in evolutionary computation (EC). At present, few EC works have been published to handle problems plagued with constraints that are expensive to compute. This paper presents a surrogate-assisted memetic co-evolutionary framework to tackle both facets of practical problems, i.e. the optimization problems having computationally expensive objectives and constraints. In contrast to existing works, the cooperative co-evolutionary mechanism is adopted as the backbone of the framework to improve the efficiency of surrogate-assisted evolutionary techniques. The idea of random-problem decomposition is introduced to handle interdependencies between variables, eliminating the need to determine the decomposition in an ad-hoc manner. Further, a novel multi-objective ranking strategy of constraints is also proposed. Empirical results are presented for a series of commonly used benchmark problems to validate the proposed algorithm.

In addition to the need for satisfying several competing objectives, many real-world applications are also dynamic and require the optimization algorithm to track the changing optimum over time. This paper proposes a new coevolutionary paradigm that hybridizes competitive and cooperative mechanisms observed in nature to solve multiobjective optimization problems and to track the Pareto front in a dynamic environment. The main idea of competitive-cooperative coevolution is to allow the decomposition process of the optimization problem to adapt and emerge rather than being hand designed and fixed at the start of the evolutionary optimization process. In particular, each species subpopulation will compete to represent a particular subcomponent of the multiobjective problem, while the eventual winners will cooperate to evolve for better solutions. Through such an iterative process of competition and cooperation, the various subcomponents are optimized by different species subpopulations based on the optimization requirements of that particular time instant, enabling the coevolutionary algorithm to handle both the static and dynamic multiobjective problems. The effectiveness of the competitive-cooperation coevolutionary algorithm (COEA) in static environments is validated against various multiobjective evolutionary algorithms upon different benchmark problems characterized by various difficulties in local optimality, discontinuity, nonconvexity, and high-dimensionality. In addition, extensive studies are also conducted to examine the capability of dynamic COEA (dCOEA) in tracking the Pareto front as it changes with time in dynamic environments.

This paper examines the comparative performance and adaptability of evolutionary, learning, and memetic strategies to different environment settings in the iterated prisoner's dilemma (IPD). A memetic adaptation framework is developed for IPD strategies to exploit the complementary features of evolution and learning. In the paradigm, learning serves as a form of directed search to guide evolving strategies to attain eventual convergence towards good strategy traits, while evolution helps to minimize disparity in performance among learning strategies. Furthermore, a double-loop incremental learning scheme (ILS) that incorporates a classification component, probabilistic update of strategies and a feedback learning mechanism is proposed and incorporated into the evolutionary process. A series of simulation results verify that the two techniques, when employed together, are able to complement each other's strengths and compensate for each other's weaknesses, leading to the formation of strategies that will adapt and thrive well in complex, dynamic environments.

Evolutionary gradient search is a hybrid algorithm that exploits the complementary features of gradient search and evolutionary algorithm to achieve a level of efficiency and robustness that cannot be attained by either techniques alone. Unlike the conventional coupling of local search operators and evolutionary algorithm, this algorithm follows a trajectory based on the gradient information that is obtain via the evolutionary process. In this paper, we consider how gradient information can be obtained and used in the context of multi-objective optimization problems. The different types of gradient information are used to guide the evolutionary gradient search to solve multi-objective problems. Experimental studies are conducted to analyze and compare the effectiveness of various implementations.

This paper reviews the different gradient-based schemes and the sources of gradient, their availability, precision and computational complexity, and explores the benefits of using gradient information within a memetic framework in the context of continuous parameter optimization, which is labeled here as memetic gradient search. In particular, we considered a quasi-Newton method with analytical gradient and finite differencing, as well as simultaneous perturbation stochastic approximation, used as the local searches. Empirical study on the impact of using gradient information showed that memetic gradient search outperformed the traditional GA and analytical, precise gradient brings considerable benefit to gradient-based local search (LS) schemes. Though gradient-based searches can sometimes get trapped in local optima, memetic gradient searches were still able to converge faster than the conventional GA.

Evolutionary algorithms are a class of stochastic search methods that attempts to emulate the biological process of evolution, incorporating concepts of selection, reproduction, and mutation. In recent years, there has been an increase in the use of evolutionary approaches in the training of artificial neural networks (ANNs). While evolutionary techniques for neural networks have shown to provide superior performance over conventional training approaches, the simultaneous optimization of network performance and architecture will almost always result in a slow training process due to the added algorithmic complexity. In this paper, we present a geometrical measure based on the singular value decomposition (SVD) to estimate the necessary number of neurons to be used in training a single-hidden-layer feedforward neural network (SLFN). In addition, we develop a new hybrid multiobjective evolutionary approach that includes the features of a variable length representation that allow for easy adaptation of neural networks structures, an architectural recombination procedure based on the geometrical measure that adapts the number of necessary hidden neurons and facilitates the exchange of neuronal information between candidate designs, and a microhybrid genetic algorithm ( microHGA) with an adaptive local search intensity scheme for local fine-tuning. In addition, the performances of well-known algorithms as well as the effectiveness and contributions of the proposed approach are analyzed and validated through a variety of data set types.

Binary representation suffers from the problem of positional dependence, where the amplitude of phenotype variation is dependent on the position of the altered genotype bits. However, this is contrary to conventional variation operations that treat each genotype bit equally. Positional dependence can be attributed to the poor locality, which results in neighboring genotypes having low correlation in the phenotype space, reducing the effectiveness of systematic local search and evolutionary search based on small mutation steps. For this purpose, this paper will propose an alternative genotype-phenotype mapping for binary representation that introduces redundancy into the mapping and removes the exponential orderings between the alleles, hence improving the locality between the genotype and phenotype search space. Empirical study conducted based on distribution, locality, and mutation innovation revealed key algorithmic characteristics of the proposed code, and its practicality is validated by comparative studies based on different benchmark optimization problems. Possible approaches to resolve the overrepresentation problem due to redundancy will be suggested, exhibiting its flexibility and variability in implementation.

This paper proposes a new multi-objective evolutionary approach for training recurrent neural networks (RNNs). The algorithm uses features of a variable length representation allowing easy adaptation of neural networks structures and a micro genetic algorithm (muGA) with an adaptive local search intensity scheme for local fine-tuning. In addition, a structural mutation (SM) operator for evolving the appropriate number of neurons for RNNs is used. Simulation results demonstrated the effectiveness of proposed method for system identification tasks.

Apart from the need to satisfy several competing objectives, many real-world applications are also sensitive to decision or environmental parameter variation which results in large or unacceptable performance variation. While evolutionary optimization techniques have several advantages over operational research methods for robust optimization, it is rarely studied by the evolutionary multi-objective (MO) optimization community. This paper addresses the issue of robust MO optimization by presenting a robust continuous MO test suite with features of noise-induced solution space, fitness landscape and decision space variation. The work presented in this paper should encourage further studies and the development of more effective algorithms for robust MO optimization.

Coevolutionary architectures have been shown to be effective ways to improve the performance of multiobjective (MO) optimization problems. This paper presents a cooperative coevolutionary algorithm for multiobjective particle swarm optimization (COMOPSO), which applies the divide-and-conquer approach to decompose decision vectors into smaller components and evolves multiple solutions in the form of cooperative subswarms. Representatives from each evolving subswarm are combined to form the solution to the whole system. The fitness of each individual is related to its ability to collaborate with individuals from other species, thereby encouraging the development of cooperative strategies. An adaptive niche sharing algorithm is introduced to handle the selection of the niche radius in a dynamic manner. Coupled with the adaptive niche sharing algorithm COMOPSO demonstrates its effectiveness and efficiency in evolving highly competitive solution sets against various MO algorithms on benchmark problems characterized by different difficulties with consistent results.

In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multiobjective optimization. Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multiobjective evolutionary algorithms in noisy environments, as well as the robustness and effectiveness of the proposed features are examined based upon five benchmark problems characterized by different difficulties in local optimality, nonuniformity, discontinuity, and nonconvexity

In this paper, a new memetic algorithm (MA) for multiobjective (MO) optimization is proposed, which combines the global search ability of particle swarm optimization with a synchronous local search heuristic for directed local fine-tuning. A new particle updating strategy is proposed based upon the concept of fuzzy global-best to deal with the problem of premature convergence and diversity maintenance within the swarm. The proposed features are examined to show their individual and combined effects in MO optimization. The comparative study shows the effectiveness of the proposed MA, which produces solution sets that are highly competitive in terms of convergence, diversity, and distribution.

This paper focuses on the design and development of a spatial evolutionary multi-agent social network (EMAS) to investigate the underlying emergent macroscopic behavioral dynamics of civil violence, as a result of the microscopic local movement and game-theoretic interactions between multiple goal-oriented agents. Agents are modeled from multi-disciplinary perspectives and their behavioral strategies are evolved over time via collective co-evolution and independent learning. Experimental results reveal the onset of fascinating global emergent phenomenon as well as interesting patterns of group movement and behavioral development. Analysis of the results provides new insights into the intricate behavioral dynamics that arises in civil upheavals. Collectively, EMAS serves as a vehicle to facilitate the behavioral development of autonomous agents as well as a platform to verify the effectiveness of various violence management policies which is paramount to the mitigation of casualties.

In addition to the need to satisfy several competing objectives, many real-world applications are also characterized by noise. In this paper, three noise-handling features, an experiential learning directed perturbation (ELDP) operator, a gene adaptation selection strategy (GASS) and a possibilistic archiving model are proposed. The ELDP adapts the magnitude and direction of variation according to past experiences for fast convergence while the GASS improves the evolutionary search in escaping from premature convergence in both noiseless and noisy environments. The possibilistic archiving model is based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multi-objective evolutionary algorithms in noisy environments as well as the robustness and effectiveness of the proposed features are examined based upon three benchmark problems characterized by different difficulties.

The bin packing problem is widely found in applications such as loading of tractor trailer trucks, cargo airplanes and ships, where a balanced load provides better fuel efficiency and safer ride. In these applications, there are often conflicting criteria to be satisfied, i.e., to minimize the bins used and to balance the load of each bin, subject to a number of practical constraints. Unlike existing studies that consider only the minimization of bins, a two-objective mathematical model for the bin packing problem with multiple constraints is formulated in this paper. Without the need of combining both objectives into a composite scalar, a hybrid multiobjective particle swarm optimization algorithm (HMOPSO) incorporating the concept of Pareto's optimality to evolve a family of solutions along the trade-off is proposed. The algorithm is also featured with bin packing heuristic, variable length representation, and specialized mutation operator to solve the multiobjective and multi-model combinatorial bin packing problem. Extensive simulations are performed on various test instances, and their performances are compared both quantitatively and statistically with other optimization methods. Each of the proposed features is also explicitly examined to illustrate their usefulness in solving the multiobjective bin packing problem.

Recent advances in evolutionary algorithms show that coevolutionary architectures are effective ways to broaden the use of traditional evolutionary algorithms. This paper presents a cooperative coevolutionary algorithm (CCEA) for multiobjective optimization, which applies the divide-and-conquer approach to decompose decision vectors into smaller components and evolves multiple solutions in the form of cooperative subpopulations. Incorporated with various features like archiving, dynamic sharing, and extending operator, the CCEA is capable of maintaining archive diversity in the evolution and distributing the solutions uniformly along the Pareto front. Exploiting the inherent parallelism of cooperative coevolution, the CCEA can be formulated into a distributed cooperative coevolutionary algorithm (DCCEA) suitable for concurrent processing that allows inter-communication of subpopulations residing in networked computers, and hence expedites the computational speed by sharing the workload among multiple computers. Simulation results show that the CCEA is competitive in finding the tradeoff solutions, and the DCCEA can effectively reduce the simulation runtime without sacrificing the performance of CCEA as the number of peers is increased

This paper investigates the use of evolution and incremental learning to find an optimal strategy in the iterative prisoner's dilemma (IPD) problem, given an environment with a collection of unknown strategies. The Meta-Lamarckian Memetic learning (MLML) scheme is conceptualized based on the biological evolution of man and his abilities to accumulate knowledge and learn from past experiences. Learning was found to be the dominant force for improvement in the short run while improvement in the long run is sustained by the process of evolution. Learning is also much more effective when carried out on an incremental basis as the games progress. A series of simulation results obtained verified that the best performance is attained when a hybrid combination of learning and evolution is carried out on an incremental basis, not just evolution or learning alone.

Many real-world applications involve complex optimization problem with various competing specifications and constraints that are often difficult, if not impossible, to be solved without the aid of powerful and efficient optimization algorithms. Although evolutionary algorithms have proven to be successful with respect to the optimization goals of proximity and diversity, their capability is bottlenecked by the evolutionary operators' abilities to deal with the complicated search spaces. Furthermore, it is well known that the algorithm's performances in different problems are sensitive to the parameter setting of the operators. In an effort to adapt the evolutionary search ability along the different regions of the search space, this paper proposes a dynamic variation operator whose parameter value is deterministically adapted during the algorithm run so as to maintain a balance between the extensive exploration in the early phase and local fine-tuning in the end phase. Comparative studies with some representative variation operators are performed on different benchmark problems to illustrate the effectiveness and efficiency of the proposed operator.

The main objectives of multiobjective evolutionary algorithms are to minimize the distance between the solution set and true Pareto front, to distribute the solutions evenly and to maximize the spread of solution set. This paper addresses these issues by presenting two features that enhance the ability of multiobjective evolutionary algorithms. The first feature is a variant of the mutation operator that adapts the mutation rate along the evolution process to maintain a balance between the introduction of diversity and local fine-tuning. In addition, this adaptive mutation operator adopts a new approach to strike a compromise between the preservation and disruption of genetic information. The second feature is a novel enhanced exploration strategy that encourages the exploration towards less populated areas and hence achieves better discovery of gaps in the generated front. This strategy also preserves nondominated solutions in the evolving population and hence gives good convergence. Comparative studies show that the proposed features are effective.