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Two-loop real-coded genetic algorithms with adaptive control of mutation step sizes

APPLIED INTELLIGENCE 2000年第3期13卷 187-204页

作者： Herrera, F Lozano, M Univ Granada Dept Comp Sci & AI E-18071 Granada Spain

genetic algorithms are adaptive methods based on natural evolution that may be used for search and optimization problems. They process a population of search space solutions with three operations: selection, crossover, and mutation. Under their initial formulation, the search space solutions are coded using the binary alphabet, however other coding types have been taken into account for the representation issue, such as real coding. The real-coding approach seems particularly natural when tackling optimization problems of parameters with variables in continuous domains. A problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of population diversity. The mutation operator is the one responsible for the generation of diversity and therefore may be considered to be an important element in solving this problem. For the case of working under real coding, a solution involves the control, throughout the run, of the strength in which real genes are mutated, i.e., the step size. This paper presents TRAMSS, a Two-loop real-coded genetic algorithm with Adaptive control of Mutation Step Sizes. It adjusts the step size of a mutation operator applied during the inner loop, for producing efficient local tuning. It also controls the step size of a mutation operator used by a restart operator performed in the outer loop, for reinitializing the population in order to ensure that different promising search zones are focused by the inner loop throughout the run. Experimental results show that the proposal consistently outperforms other mechanisms presented for controlling mutation step sizes, offering two main advantages simultaneously, better reliability and accuracy.

关键词： real-coded genetic algorithms premature convergence mutation operator

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Robust confidence intervals applied to crossover operator for real-coded genetic algorithms

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SOFT COMPUTING 2008年第8期12卷 809-833页

作者： Ortiz-Boyer, Domingo Hervas-Martinez, Cesar Garcia-Pedrajas, Nicolas Univ Cordoba Dept Comp & Numer Anal Cordoba Spain

In this work we propose a new approach to crossover operators for real-coded genetic algorithms based on robust confidence intervals. These confidence intervals are an alternative to standard confidence intervals. In this paper, they are used for localising the search regions where the best individuals are placed. Robust confidence intervals use robust localization and dispersion estimators that are highly recommendable when the distribution of the random variables is not known or is distorted. Both situations are likely when we are dealing with the best individuals of the population, especially if the problem under study is multimodal. The performance of the crossovers based on robust intervals is evaluated using a well characterised set of optimisation problems. We have chosen problems with different features of modality, separability, regularity, and correlation among their variables. The results show that the performance of the crossovers based on robust confidence intervals is less dependent on the problem than the performance of the crossovers based on Gaussian confidence intervals. We have also made comparisons between several standard crossovers that show very interesting results, which support the idea underlying the defined operators.

关键词： evolutionary algorithms real-coded genetic algorithms robust confidence intervals optimisation methods

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Optimal lens design by real-coded genetic algorithms using UNDX

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2000年第2-4期186卷 483-497页

作者： Ono, I Kobayashi, S Yoshida, K Tokyo Inst Technol Yokohama Kanagawa 2268501 Japan Univ Tokushima Tokushima 7708506 Japan Nikon Inc Shinagawa 1408601 Japan

This paper presents new lens optimization methods based on real-coded genetic algorithms (GAs). We take advantage of GA's capability of global optimization and multi-objective optimization against two serious problems in conventional lens optimization techniques: (1) choosing a starting point by trial and error, and (2) combining multiple criteria to a single criterion. In this paper, two criteria for lenses, the resolution and the distortion, are considered. First, we propose a real-coded GA that optimizes a single criterion, a weighted sum of the resolution and the distortion. To overcome a problem of the difficulty in generating feasible lenses especially in large-scale problems, we introduce a feasibility enforcement operator to modify an infeasible solution into a feasible one. By applying the proposed method to some small-scale problems, we show that the proposed method can find empirically optimal and suboptimal lenses. We also apply the proposed method to some relatively large-scale problems and show that the proposed method can effectively work under large-scale problems. Next, regarding the lens design problem as a multi-objective optimization problem, we propose a real-coded multi-objective GA that explicitly optimizes the two criteria, the resolution and the distortion. We show the effectiveness of the proposed method in multi-objective lens optimization by applying it to a three-element lens design problem. (C) 2000 Published by Elsevier Science S.A. All rights reserved.

关键词： lens design real-coded genetic algorithms global optimization multi-objective optimization UNDX MGG

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Global and local real-coded genetic algorithms based on parent-centric crossover operators

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2008年第3期185卷 1088-1113页

作者： Garcia-Martinez, C. Lozano, M. Herrera, F. Molina, D. Sanchez, A. M. Univ Cordoba Dept Comp & Numer Anal E-14071 Cordoba Spain Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Cadiz Dept Software Engn Cadiz 11002 Spain Univ Granada Dept Software Engn E-18071 Granada Spain

Parent-centric real-parameter crossover operators create the offspring in the neighbourhood of one of the parents, the female parent. The other parent, the male one, defines the range of the neighbourhood. With the aim of improving the behaviour of these crossover operators, we present three processes that are performed before their application. First, a female and male differentiation process determines the individuals in the population that may become female or/and male parents. Then, two different selection mechanisms choose the female and male parents from each group. In addition, we tackle the election of the most adequate evolution model to take out profit from these parent selection mechanisms. The experimental results confirm that these three processes may enhance the operation of the parent-centric crossover operators. (C) 2006 Elsevier B.V. All rights reserved.

关键词： real-coded genetic algorithms steady-state genetic algorithms parent selection mechanism parent-centric crossover operators chromosome differentiation hybrid real-coded genetic algorithms

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Myocardial border detection from ventriculograms using support vector machines and real-coded genetic algorithms

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COMPUTERS IN BIOLOGY AND MEDICINE 2010年第4期40卷 446-455页

作者： Vera, Miguel Bravo, Antonio Medina, Ruben Univ Nacl Expt Tachira Grp Bioingn San Cristobal 5001 Venezuela Univ Los Andes Tachira Fis Lab Dept Ciencias San Cristobal 5001 Venezuela Univ Los Andes Grp Ingn Biomed GIBULA Fac Ingn Merida 5101 Venezuela

In this research a two step method for left ventricle segmentation based on landmark detection and evolutionary snakes is reported. The proposed approach is applied to human heart angiograms. Several anatomical landmarks located on the left ventricle are obtained using support vector machines. The training stage is performed based on a set of windows of size 31 x 31 including landmarks and non-landmarks pixel patterns. The support vector machines use a radial basis function kernel and the structural risk minimization principle as the inference rule. During the training stage, no false positives are obtained and during the detection stage a 97.94% of recognition is attained. The estimated landmark location is used for constructing an approximate myocardial border. This contour is a deformable model that is optimized using a real-coded genetic algorithm. A validation is performed by comparing the estimated contours with respect to contours manually traced by two cardiologists. From this validation stage the maximum of the average contour error considering 6 angiographic sequences (a total of 178 images) is 4.93%. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Human heart Anatomical landmarks Myocardial border Left ventricle Patterns classification Support vectors machines real-coded genetic algorithms Deformable models

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Improving crossover operator for real-coded genetic algorithms using virtual parents

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JOURNAL OF HEURISTICS 2007年第3期13卷 265-314页

作者： Ortiz-Boyer, Domingo Hervas-Martinez, Cesar Garcia-Pedrajas, Nicolas Univ Cordoba Dept Comp & Numer Anal Cordoba Spain

The crossover operator is the most innovative and relevant operator in real-coded genetic algorithms. In this work we propose a new strategy to improve the performance of this operator by the creation of virtual parents obtained from the population parameters of localisation and dispersion of the best individuals. The idea consists of mating these virtual parents with individuals of the population. In this way, the offspring are created in the most promising regions. This strategy has been incorporated into several crossover operators. After analysing the results we can conclude that this strategy significantly improves the performance of the algorithm in most problems analysed.

关键词： real-coded genetic algorithms crossover operator optimisation methods

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Hybrid crossover operators for real-coded genetic algorithms:: an experimental study

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SOFT COMPUTING 2005年第4期9卷 280-298页

作者： Herrera, F Lozano, M Sánchez, AM Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Vigo Escuela Super Ingn Informat Dept Informat Orense 32004 Spain

Most real-coded genetic algorithm research has focused on developing effective crossover operators, and as a result, many different types have been proposed. Some forms of crossover operators are more suitable to tackle certain problems than others, even at the different stages of the genetic process in the same problem. For this reason, techniques which combine multiple crossovers have been suggested as alternative schemes to the common practice of applying only one crossover model to all the elements in the population. Therefore, the study of the synergy produced by combining the different styles of the traversal of solution space associated with the different crossover operators is an important one. The aim is to investigate whether or not the combination of crossovers perform better than the best single crossover amongst them. In this paper we have undertaken an extensive study in which we have examined the synergetic effects among real-parameter crossover operators with different search biases. This has been done by means of hybrid real-parameter crossover operators, which generate two offspring for every pair of parents, each one with a different crossover operator. Experimental results show that synergy is possible among real-parameter crossover operators, and in addition, that it is responsible for improving performance with respect to the use of a single crossover operator.

关键词： real-coded genetic algorithms crossover operator hybrid crossover operators

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A comparison study of self-adaptation in evolution strategies and real-coded genetic algorithms

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EVOLUTIONARY COMPUTATION 2001年第2期9卷 223-241页

作者： Kita, H Natl Inst Acad Degrees Fac Univ Evaluat & Res Bunkyo Ku Tokyo 1120012 Japan

This paper discusses the self-adaptive mechanisms of evolution strategies (ES) and real-coded genetic algorithms (RCGA) for optimization in continuous search spaces. For multi-membered evolution strategies, a self-adaptive mechanism of mutation parameters has been proposed by Schwefel. It introduces parameters such as standard deviations of the normal distribution for mutation into the genetic code and lets them evolve by selection as well as the decision variables. In the RCGA, Crossover or recombination is used mainly for search. It utilizes information on several individuals to generate novel search points, and therefore, it can generate offspring adaptively according to the distribution of parents without any adaptive parameters. The present paper discusses characteristics of these two self-adaptive mechanisms through numerical experiments. The self-adaptive characteristics such as translation, enlargement, focusing, and directing of the distribution of children generated by the ES and the RCGA are examined through experiments.

关键词： self-adaptive mutation evolution strategies real-coded genetic algorithms unimodal normal distribution crossover genetic algorithms

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An Automatic Modeling System of the Reaction Mechanisms for Chemical Vapor Deposition Processes Using real-coded genetic algorithms

An Automatic Modeling System of the Reaction Mechanisms for ...

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EuroCVD-18 Conference (EuroCVD)

作者： Takahashi, Takahiro Nakai, Hiroyuki Kinpara, Hiroki Ema, Yoshinori Shizuoka Univ Fac Engn Dept Elect & Elect Engn Naka Ku Hamamatsu Shizuoka 4328561 Japan

The identification of appropriate reaction models is very helpful for developing chemical vapor deposition (CVD) processes. In this study, we have developed an automatic system to model reaction mechanisms in the CVD processes by analyzing the experimental results, which are cross-sectional shapes of the deposited films on substrates with micrometer- or nanometer-sized trenches. We designed the inference engine to model the reaction mechanism in the system by the use of real-coded genetic algorithms (RCGAs). We studied the dependence of the system performance on two methods using simple genetic algorithms (SGAs) and the RCGAs;the one involves the conventional GA operators and the other involves the blend crossover operator (BLX-alpha). Although we demonstrated that the systems using both the methods could successfully model the reaction mechanisms, the RCGAs showed the better performance with respect to the accuracy and the calculation cost for identifying the models.

关键词： Chemical Vapor Deposition Reaction Mechanism genetic algorithms real-coded genetic algorithms Automatic Modeling Simulation

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Efficient real-coded genetic algorithms with flexible-step crossover

Efficient real-coded genetic algorithms with flexible-step c...

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IEEE Congress on Evolutionary Computation

作者： Mutoh, A Kato, S Itoh, H Nagoya Inst Technol Showa Ku Nagoya Aichi 4668555 Japan

ISBN: (纸本)0780393635

real-coded genetic algorithms (GAs) are effective methods for function optimization. Generally speaking, the major crossover methods used in realcoded GAs require a large execution time for calculating the fitness of many children at each crossover. Thus, a new crossover method is needed for searching such a large search space efficiently. A novel crossover method that generates children stepwise is proposed and applied to the conventional generation-alternation model. In experiments based on standard test functions and actual problems, the proposed model found an optimal solution 30-50% faster than did the conventional model.

关键词： genetic algorithms search problems fitness function flexible-step crossover function optimization generation-alternation model optimal solution real-coded genetic algorithms search space genetic Algorithm (GA) execution time tabu search genetic Crossing Over optimal solution

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