Improving Genetic Algorithms' Efficiency Using Intelligent Fitness Functions
By Jason Cooper, C. Hinde. 2003
19 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Marc-André Zöller
- Leslie S. Smith
- Marco F. Huber
- Iffat A. Gheyas
- Li Yang
- Abdallah Shami
- Jessie Y.C. Chen
- Tarek Amr Abdallah
Citations in the corpus
- Survey on Automated Machine Learning (2019, background): “Due to the somewhat random and unguided creation of new generations, evolutionary algorithms are not very efficient concerning the number of objective function evaluations (Cooper and Hinde, 2003) in comparison to Bayesian optimization.”
- Feature subset selection in large dimensionality domains (2010, background): “Second , Cooper and Hinde (2003) report that evolutionary algorithms 63 spend approximately a third of the time testing on already tested candidate solutions [39].”