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Improving Genetic Algorithms’ Efficiency Using Intelligent Fitness Functions

Cooper, Jason ; Hinde, Chris

Developments in Applied Artificial Intelligence, 2003, p.636-643 [Periódico revisado por pares]

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Título:
Improving Genetic Algorithms’ Efficiency Using Intelligent Fitness Functions
Autor: Cooper, Jason ; Hinde, Chris
Assuntos: Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Exact sciences and technology ; Genetic Algorithms ; Learning and adaptive systems
É parte de: Developments in Applied Artificial Intelligence, 2003, p.636-643
Descrição: Genetic Algorithms are an effective way to solve optimisation problems. If the fitness test takes a long time to perform then the Genetic Algorithm may take a long time to execute. Using conventional fitness functions Approximately a third of the time may be spent testing individuals that have already been tested. Intelligent Fitness Functions can be applied to improve the efficiency of the Genetic Algorithm by reducing repeated tests. Three types of Intelligent Fitness Functions are introduced and compared against a standard fitness function The Intelligent Fitness Functions are shown to be more efficient.
Títulos relacionados: Lecture Notes in Computer Science
Editor: Berlin, Heidelberg: Springer Berlin Heidelberg
Idioma: Inglês

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