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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Vydané v:	IEEE transactions on evolutionary computation Ročník 15; číslo 5; s. 692 - 714
Hlavní autori:	McGinley, Brian, Maher, John, O'Riordan, Colm, Morgan, Fearghal
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.10.2011 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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 the efficient optimization of multimodal fitness landscapes. A new methodology is introduced for determining standard population diversity (SPD) and an original measure of healthy population diversity (HPD) is proposed. The SPD measure is employed to adapt crossover and mutation, while selection pressure is controlled by adapting tournament size according to HPD. In addition to selection pressure control, ACROMUSE tournament selection selects individuals according to healthy diversity contribution rather than fitness. This proposed selection mechanism simultaneously promotes diversity and fitness within the population. The performance of ACROMUSE is evaluated using various multimodal benchmark functions. Statistically significant results are presented comparing ACROMUSEs fitness and diversity performance to that of several other GAs. By maintaining a diverse population of healthy individuals, ACROMUSE responds to fitness landscape change by restoring better fitness scores faster than other GAs. Analysis of the adaptive operators illustrates that the key benefit of ACROMUSE is the synergy of the operators working together to achieve an effective balance between exploration and exploitation.
AbstractList	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 the efficient optimization of multimodal fitness landscapes. A new methodology is introduced for determining standard population diversity (SPD) and an original measure of healthy population diversity (HPD) is proposed. The SPD measure is employed to adapt crossover and mutation, while selection pressure is controlled by adapting tournament size according to HPD. In addition to selection pressure control, ACROMUSE tournament selection selects individuals according to healthy diversity contribution rather than fitness. This proposed selection mechanism simultaneously promotes diversity and fitness within the population. The performance of ACROMUSE is evaluated using various multimodal benchmark functions. Statistically significant results are presented comparing ACROMUSEs fitness and diversity performance to that of several other GAs. By maintaining a diverse population of healthy individuals, ACROMUSE responds to fitness landscape change by restoring better fitness scores faster than other GAs. Analysis of the adaptive operators illustrates that the key benefit of ACROMUSE is the synergy of the operators working together to achieve an effective balance between exploration and exploitation.
Author	Morgan, Fearghal McGinley, Brian Maher, John O'Riordan, Colm
Author_xml	– sequence: 1 givenname: Brian surname: McGinley fullname: McGinley, Brian email: brianmcginley@gmail.com organization: Bio-Inspired and Reconfigurable Computing Research Group, National University of Ireland (NUI), Galway, Ireland – sequence: 2 givenname: John surname: Maher fullname: Maher, John email: john.maher@nuigalway.ie organization: Bio-Inspired and Reconfigurable Computing Research Group, National University of Ireland (NUI), Galway, Ireland – sequence: 3 givenname: Colm surname: O'Riordan fullname: O'Riordan, Colm email: colm.oriordan@nuigalway.ie organization: Computational Intelligence Research Group, National University of Ireland (NUI), Galway, Ireland – sequence: 4 givenname: Fearghal surname: Morgan fullname: Morgan, Fearghal email: fearghal.morgan@nuigalway.ie organization: Bio-Inspired and Reconfigurable Computing Research Group, National University of Ireland (NUI), Galway, Ireland
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Snippet	This paper presents ACROMUSE, a novel genetic algorithm (GA) which adapts crossover, mutation, and selection parameters. ACROMUSEs objective is to create and...
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SubjectTerms	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
Title	Maintaining Healthy Population Diversity Using Adaptive Crossover, Mutation, and Selection
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