Memetic computation (MC) has emerged recently as a new paradigm of efficient algorithms for solving the hardest optimization problems. On the other hand, artificial bees colony (ABC) algorithms demonstrate good performances when solving continuous and combinatorial optimization problems. This study tries to use these technologies under the same roof. As a result, a memetic ABC (MABC) algorithm has been developed that is hybridized with two local search heuristics: the Nelder-Mead algorithm (NMA) and the random walk with direction exploitation (RWDE). The former is attended more towards exploration, while the latter more towards exploitation of the search space. The stochastic adaptation rule was employed in order to control the balancing between exploration and exploitation. This MABC algorithm was applied to a Special suite on Large Scale Continuous Global Optimization at the 2012 IEEE Congress on Evolutionary Computation. The obtained results the MABC are comparable with the results of DECC-G, DECC-G*, and MLCC.

This paper presents an approach for reconstruction of procedural three-dimensional models of woody plants (trees). The used procedural tree model operates by recursively computing all building parts of a three-dimensional tree structure by applying a fixed procedure on a given large set of numerically coded input parameters. The parameterized procedural model can later be used for computer animation.Reconstruction of a parameterized procedural model from images is done by differential evolution algorithm which evolves this model by fitting a set of its rendered images to a set of given reference images. The comparison is done on pixel level of the images through the integration of distances to the nearest similar pixels. The obtained results show that the presented approach is viable for modeling of woody plants for computer animation by evolution of the numerically coded procedural model. (C) 2011 Elsevier B.V. All rights reserved.

This paper presents a differential evolution (DE) based approach to chess evaluation function tuning. DE with opposition-based optimization is employed and upgraded with a history mechanism to improve the evaluation of individuals and the tuning process. The general idea is based on individual evaluations according to played games through several generations and different environments. We introduce a new history mechanism which uses an auxiliary population containing good individuals. This new mechanism ensures that good individuals remain within the evolutionary process, even though they died several generations back and later can be brought back into the evolutionary process. In such a manner the evaluation of individuals is improved and consequently the whole tuning process.

This paper presents an approach for recognition of procedural threedimensional models of woody plants (trees). The used procedural tree model operates by building a three-dimensional structure of a tree by applying a fixed procedure on a given set of numerically-coded input parameters. The parameterized procedural model can later be used for computer animation. Recognition of a parameterized procedural model, from the photographic images, is done by differential evolution algorithm which evolves this model by fitting a set of its rendered images to a set of given photographic images. The comparison is done on a pixel level of the images through the integration of distances to the nearest similar pixels. The obtained results show that the presented approach is viable for modeling of woody plants for computer animation by evolution of the numerically-coded procedural model.

In this paper we investigate a Self-Adaptive Differential Evolution algorithm (jDE) where F and CR control parameters are self-adapted and a multi-population method with aging mechanism is used. The performance of the jDE algorithm is evaluated on the set of benchmark functions provided for the CEC 2009 special session on evolutionary computation in dynamic and uncertain environments.

Zero is an experimental statically typed, fully object-oriented reflective programming language. Reflective features cover introspection as well as structural and behavioural reflection. The reflective facilities include safe method and class replacements and detailed modification of methods. These enable Zero programs to quickly accommodate to run-time requirements. Behavioural reflection is realised using handlers (hooks), which may be attached to all language constructs based on closures. Zero provides an efficient static typing system with run-time extensions. Methods are first class values and are represented as objects when such representation is required. By using such representation, Zero provides elegant use of statically typed higher-order methods.

This paper presents differential evolution with self-adaptation and local search for constrained multiobjective optimization algorithm (DECMOSA-SQP), which uses the self-adaptation mechanism from DEMOwSA algorithm presented at CEC 2007 and a SQP local search. The constrained handling mechanism is also incorporated in the new algorithm. Assessment of the algorithm using CEC 2009 special session and competition on constrained multiobjective optimization test functions is presented. The functions are composed of unconstrained and constrained problems. Their results are assessed using the IGD metric. Based on this metric, algorithm strengths and weaknesses are discussed.

Recognition of a parametrized procedural model from the photographic images is done by differential evolution algorithm which evolves the parametrized procedural model by fitting a set of its rendered images to a set of given photographic images. The comparison is done on a pixel level of the images by integrating distances to nearest similar pixels. The obtained results show that the presented approach is viable for modeling of natural trees in computer animation by evolution of the numericallycoded procedural model.

In this paper, an optimization algorithm is formulated and its performance assessment for large scale global optimization is presented. The proposed algorithm is named DEwSAcc and is based on Differential Evolution (DE) algorithm, which is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces. The original DE is extended by log-normal self-adaptation of its control parameters and combined with cooperative co-evolution as a dimension decomposition mechanism. Experimental results are given for seven high-dimensional test functions proposed for the Special Session on Large Scale Global Optimization at 2008 IEEE World Congress on Computational Intelligence.

This chapter describes an algorithm for the tuning of a chess program which is based on Differential Evolution using adaptation and opposition based optimization mechanisms. The mutation control parameter F is adapted according to the deviation of search parameters in each generation. Opposition-based optimization is included in the initialization, and in the evolutionary process itself. In order to demonstrate the behaviour of our algorithm we tuned our BBChess chess program with a combination of adaptive and opposition-based optimization. Tuning results show that adaptive optimization with an opposition-based mechanism increases the robustness of the algorithm and has a comparable convergence to the algorithm which uses only adaptation optimization.

In this paper we present experimental results of self-adaptive differential evolution algorithm hybridized with a local search method. The results of the proposed hybrid algorithm are evaluated on a set of benchmark functions provided by the IEEE Congress on Evolutionary Computation (CEC 2008) special session on Large Scale Global Optimization. Performance comparison of our algorithm with other algorithms is reported.

In this paper we investigate a Self-Adaptive Differential Evolution algorithm (jDEdynNP-F) where F and CR control parameters are self-adapted and a population size reduction method is used. Additionally the proposed jDEdynNP-F algorithm uses a mechanism for sign changing of F control parameter with some probability based on the fitness values of randomly chosen vectors, which are multiplied by the F control parameter (scaling factor) in the mutation operation of DE algorithm. The performance of the jDEdynNP-F algorithm is evaluated on the set of 7 benchmark functions provided for the CEC'2008 special session on high-dimensional real-parameter optimization.

In this paper we present an experimental analysis showing that the self-adaptation of control parameters plays an important role in the multiobjective optimization process (refer to Figure 1 for notion of multiobjective optimality). Experimental results of a self-adaptive differential evolution algorithm are evaluated on the set of benchmark functions provided for the CEC 2007 Special session on Performance Assessment & Competition on Multi-objective Optimization Algorithms, as seen in Tables 2-7. Self-adaptation is proven to statistically outperform fixed parameters, using t-test on the empirical results in these tables. The values of control parameters are encoded in each individual (see Figure 2) and changed during the optimization process. They depend on the nature of the problem being solved, as can be seen in Table 1 and Figures 3 and 4 which show how using self-adaptation good control parameters are obtained to improve the search results.

In this paper we present performance evaluation of the original differential evolution (DE) and our self-adaptive differential evolution algorithm. The algorithm, which we named jDE [4, 6], uses a self-adapting control parameter mechanism on control parameters F and CR during the optimization process. The performance of the algorithm is evaluated on a set of benchmark functions provided for the CEC 2008 special session on high-dimensional real-parameter optimization [18].To solve high-dimensional problems [12, 18], cooperative coevolution [13, 15] is used in literature. Liu et al. [11] uses FEP (fast evolutionary programming) with cooperative coevolution (FEPCC) to speed-up convergence rates on the large-scale problems, Bergh and Engelbrecht [20] applies a Cooperative Approach to Particle Swarm Optimisation (PSO), Yang, Tang, and Yao uses differential evolution with cooperative coevolution (DECC) [21].The performance of the jDE and the original DE algorithms is evaluated on a set of seven benchmark functions provided for the CEC 2008 special session [18] each for three dimensions D = 100, D = 500, and D = 1000. The obtained results (see the tables and figure) show that the self-adaptive jDE algorithm outperforms the original DE algorithm for each tested function.

The main goal of this chapter is to present an analysis of how self-adaptive control parameters are being changed during the current evolutionary process. We present a comparison of two distinct self-adaptive control parameters' mechanisms, both using Differential Evolution (DE). The first mechanism has recently been proposed in the jDE algorithm, which uses self-adaptation for F and CR control parameters. In the second one, we integrated the well known self-adaptive mechanism from Evolution Strategies (ES) into the original DE algorithm, also for the F and CR control parameters. Both mechanisms keep the third DE control parameter NP fixed during the optimization process. They both use the same DE strategy, same mutation, crossover, and selection operations, even the same initial population, and they both use self-adaptation at individual level.

We present a framework for the simulation of forests, where a complex application system simulates a spontaneous afforestation process. Within this virtual environment, trees can be seen to grow over several centuries. The obtained simulation results are used to animate ecosystem development, where trees struggle for survival. The visualization of trees is speeded up so that the models of trees have progressively lower-details proportional to the distance from a certain point of view. The growth of individual trees is also animated, from the development of branch complexity to per-leaf precision to allow a very realistic perception of the emerging ecosystem.

Differential Evolution (DE) is a simple yet powerful evolutionary algorithm for global optimization. DEMO is well-known algorithm for multiobjective optimization. This paper presents empirical results on a set of (CEC’2007) benchmark functions. The results were obtained by our self-adaptive differential evolution algorithm, called MOjDE, which is an extension of DEMO algorithm with self-adaptive control parameters, first proposed by J. Brest et al. [3]. The paper outlines that the self-adative algorithm MOjDE gives better performance results than DEMO.

Differential evolution (DE) has been shown to be a simple, yet powerful, evolutionary algorithm for global optimization for many real problems. Adaptation, especially self-adaptation, has been found to be highly beneficial for adjusting control parameters, especially when done without any user interaction. This paper presents differential evolution algorithms, which use different adaptive or self-adaptive mechanisms applied to the control parameters. Detailed performance comparisons of these algorithms on the benchmark functions are outlined.

In this paper we present an experimental analysis Showing that the population size NP is an important control parameter in the differential evolution algorithmDifferential Evolution (DE) [16, 17, 15, 11, 12, 10, 18] has been proven to be a powerful evolutionary algorithm for global optimization in many real problems [13, 14]. Although the D algorithm has been proven to be a simple yet powerful evoluionary algorithm for optimizing continuous functions, users are still faced with the problem of preliminary testing and hand tuning of the evolutionary parameters prior to commencing th actual optimization process [18].Different problems usually require different settings for the control parameters. Self-adaptation allows an evolution strategy to adapt itself to any general class of problems by reconfiguing itself accordingly and without any user interaction [1, 2, 7]. ased on the experiment in [5], the necessity of changing control parameters during the optimization process is also confirmed. In literature, self-adaptation is usually applied to the F and CR control parameters [5, 4], where F is a scaling factor and CR is a crossover rateIn our previous paper [6], a performance of the self-adaptive differential evolution Mgorithni is evaluated on a set of 24 benchmark functions provided for the CEC2006 Special session on constrained real-parameter optimization [9]. The method in [6] extends individuals that have not only decision variables (but also control parameters F and CR. These parameters are changed/optimized by DES too. The authors utilize lexico-graphic ordering in which the constraint violation precedes the objective function to solve constrained problems. In [6] the control parameter NP is set to 200.The number of evaluations of an evolutionary algorithm is computed as a product of N P and the number of generations. The third control parameter N P gets usually less attention in literature, mainly because it affects only the finish time of the evolutionary process when the number of generations is fixed.The scenario is quite different when the number of evaluations is fixed (an algorithm stops after the number of performed evaluations has reached the predetermined value). The question is how to determine N P to get the best results.In this paper experimental results of a self-adaptive differential evolution algorithm are evaluated on the set of 24 benchmark functions provided for the CEC2006 Special session on constrained real-parameter optimization. We especially focus on the third DE control parameter, namely N P.The results confirm our assumption that NP is an important parameter of the DE algorithm. For a set of benchmark functions the best value of the population size seems to be about 120. The optimal value depends on the nature of the problem being solved.The results show that it would be reasonable to include the N P parameter into the adaptation or even self-adaptation scheme as well.

We describe an efficient technique for adapting control parameter settings associated with differential evolution (DE). The DE algorithm has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters, which are kept fixed throughout the entire evolutionary process. However, it is not an easy task to properly set control parameters in DE. We present an algorithm-a new version of the DE algorithm-for obtaining self-adaptive control parameter settings that show good performance on numerical benchmark problems. The results show that our algorithm with self-adaptive control parameter settings is better than, or at least comparable to, the standard DE algorithm and evolutionary algorithms from literature when considering the quality of the solutions obtained.