Maintaining Healthy Population Diversity Using Adaptive Crossover, Mutation, and Selection

This paper presents ACROMUSE, a novel genetic algorithm (GA) which adapts crossover, mutation, and selection parameters. ACROMUSEs objective is to create and maintain a diverse population of highly-fit (healthy) individuals, capable of adapting quickly to fitness landscape change and well-suited to...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 15; H. 5; S. 692 - 714
Hauptverfasser:	McGinley, Brian, Maher, John, O'Riordan, Colm, Morgan, Fearghal
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.10.2011 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Algorithmics. Computability. Computer arithmetics Applied sciences Combinatorics Combinatorics. Ordered structures Computer science; control theory; systems Convergence Crossovers Exact sciences and technology Fitness Genetic algorithm parameter adaptation Genetic algorithms Genetics Graph theory healthy population diversity Information retrieval. Graph Landscapes Mathematical analysis Mathematics Mutation Mutations Operators Optimization Pressure measurement Sciences and techniques of general use Size measurement Studies Theoretical computing Statistical analysis Genetic algorithm Multimodality Evolutionary algorithm Local optimum Ecological niche healthy population diversity Graph theory Adaptive method Tournament Fitness landscape Genetic algorithm parameter adaptation
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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