Journal ArticleDOI
A Comparison Study of Self-Adaptation in Evolution Strategies and Real-Coded Genetic Algorithms
TLDR
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.Abstract:
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.read more
Citations
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Journal ArticleDOI
A computationally efficient evolutionary algorithm for real-parameter optimization
TL;DR: The proposed generic parent-centric recombination operator (PCX) and a steady-state, elite-preserving, scalable, and computationally fast population-alteration model (G3 model) are proposed and found to consistently and reliably perform better than all other methods used in the study.
Journal ArticleDOI
A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study
TL;DR: In this article, a taxonomy of real-coded genetic algorithms based on real-number representation is presented, where the crossover operator is used to generate the genes of the offspring of the parent from the parents.
Journal ArticleDOI
A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization
Zixing Cai,Yong Wang +1 more
TL;DR: The empirical evidence suggests that the new approach is robust, efficient, and generic when handling linear/nonlinear equality/inequality constraints and the best, mean, and worst objective function values and the standard deviations.
Journal ArticleDOI
Real-coded memetic algorithms with crossover hill-climbing
TL;DR: Experimental results show that, for a wide range of problems, the method proposed here consistently outperforms other real-coded memetic algorithms which appeared in the literature.
Journal ArticleDOI
On self-adaptive features in real-parameter evolutionary algorithms
Hans-Georg Beyer,Kalyanmoy Deb +1 more
TL;DR: The postulations and population variance calculations explain why self-adaptive genetic algorithms and evolution strategies have shown similar performance in the past and also suggest appropriate strategy parameter values, which must be chosen while applying and comparing different SA-EAs.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.