GA-Selection Revisited from an ES-Driven Point of View

Michael Affenzeller; Stefan Wagner; Stephan Winkler

doi:10.1007/11499305_27

GA-Selection Revisited from an ES-Driven Point of View

Michael Affenzeller, Stefan Wagner, Stephan Winkler

Research output: Contribution to journal › Conference article › peer-review

5 Citations (Scopus)

Abstract

Whereas the selection concept of Genetic Algorithms (GAs) and Genetic Programming (GP) is basically realized by the selection of above-average parents for reproduction, Evolution Strategies (ES) use the fitness of newly evolved offspring as the basis for selection (survival of the fittest due to birth surplus). This contribution proposes a generic and enhanced selection model for GAs considering selection aspects of population genetics and ES. Some selected aspects of these enhanced techniques are discussed exemplarily on the basis of standardized bench-mark problems.

Original language	English
Pages (from-to)	262-271
Number of pages	10
Journal	Lecture Notes in Computer Science
Volume	3562
Issue number	PART II
DOIs	https://doi.org/10.1007/11499305_27
Publication status	Published - Jun 2005

Keywords

hybrid metaheuristics
genetic algorithms
eveolution strategies
selection

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10.1007/11499305_27

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abstract = "Whereas the selection concept of Genetic Algorithms (GAs) and Genetic Programming (GP) is basically realized by the selection of above-average parents for reproduction, Evolution Strategies (ES) use the fitness of newly evolved offspring as the basis for selection (survival of the fittest due to birth surplus). This contribution proposes a generic and enhanced selection model for GAs considering selection aspects of population genetics and ES. Some selected aspects of these enhanced techniques are discussed exemplarily on the basis of standardized bench-mark problems.",

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AB - Whereas the selection concept of Genetic Algorithms (GAs) and Genetic Programming (GP) is basically realized by the selection of above-average parents for reproduction, Evolution Strategies (ES) use the fitness of newly evolved offspring as the basis for selection (survival of the fittest due to birth surplus). This contribution proposes a generic and enhanced selection model for GAs considering selection aspects of population genetics and ES. Some selected aspects of these enhanced techniques are discussed exemplarily on the basis of standardized bench-mark problems.

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KW - genetic algorithms

KW - eveolution strategies

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KW - eveolution strategies

KW - selection

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DO - 10.1007/11499305_27

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JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

IS - PART II

ER -

GA-Selection Revisited from an ES-Driven Point of View

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Keywords

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