Genetic Algorithms and Evolution Strategies to Solving Numerical Equations of Hydraulic Problems

Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. Evolutionary algorithms are stochastic search methods that try to emulate Darwin’s principle of natural evolution. There are (at least) four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyzes present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. The paper presents implementation examples that show the working principles of evolutionary algorithms.

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Evolutionary Algorithms in Theory and Practice:984Thomas Bäck. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, 1996. xii + 314 pp, ISBN: 0‐19‐509971‐0

Kybernetes ◽

10.1108/k.1998.27.8.979.4 ◽

1998 ◽

Vol 27 (8) ◽

pp. 979-980 ◽

Cited By ~ 10

Author(s):

John Galletly

Keyword(s):

Genetic Algorithms ◽

Evolutionary Algorithms ◽

Evolutionary Programming ◽

Theory And Practice ◽

Evolution Strategies ◽

Oxford University ◽

Oxford University Press

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An Adaptive Sharing Elitist Evolution Strategy for Multiobjective Optimization

Evolutionary Computation ◽

10.1162/106365603322519297 ◽

2003 ◽

Vol 11 (4) ◽

pp. 417-438 ◽

Cited By ~ 16

Author(s):

Lino Costa ◽

Pedro Oliveira

Keyword(s):

Genetic Algorithms ◽

Multiobjective Optimization ◽

Evolution Strategy ◽

Evolution Strategies ◽

New Approach ◽

The Past ◽

Almost All ◽

Single Objective

Almost all approaches to multiobjective optimization are based on Genetic Algorithms (GAs), and implementations based on Evolution Strategies (ESs) are very rare. Thus, it is crucial to investigate how ESs can be extended to multiobjective optimization, since they have, in the past, proven to be powerful single objective optimizers. In this paper, we present a new approach to multiobjective optimization, based on ESs. We call this approach the Multiobjective Elitist Evolution Strategy (MEES) as it incorporates several mechanisms, like elitism, that improve its performance. When compared with other algorithms, MEES shows very promising results in terms of performance.

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A Comparison Study of Self-Adaptation in Evolution Strategies and Real-Coded Genetic Algorithms

Evolutionary Computation ◽

10.1162/106365601750190415 ◽

2001 ◽

Vol 9 (2) ◽

pp. 223-241 ◽

Cited By ~ 64

Author(s):

Hajime Kita

Keyword(s):

Genetic Algorithms ◽

The Self ◽

Evolution Strategies ◽

Search Spaces ◽

Adaptive Parameters ◽

Decision Variables ◽

Adaptive Mechanisms ◽

Self Adaptation ◽

Real Coded Genetic Algorithms ◽

Self Adaptive

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.

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