On the performance of fitness uniform selection for non-deceptive problems

Authors:
Ruben Ramirez-Padron;Feras Batarseh;Kyle Heyne;Annie S. Wu;Avelino Gonzalez
Affiliations:
University of Central Florida, Orlando, FL;University of Central Florida, Orlando, FL;University of Central Florida, Orlando, FL;University of Central Florida, Orlando, FL;University of Central Florida, Orlando, FL
Venue:
Proceedings of the 48th Annual Southeast Regional Conference
Year:
2010

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Abstract

Genetic algorithms (GAs) are probabilistic search techniques inspired by natural evolution. Selection schemes are used by GAs to choose individuals from a population to breed the next generation. Proportionate, ranking and tournament selection are standard selection schemes. They focus on choosing individuals with high fitness values. Fitness Uniform Selection Scheme (FUSS) is a recently proposed selection scheme that focuses on fitness diversity. FUSS have shown better performance than standard selection schemes for deceptive and NP-complete problems. In general, it is difficult to determine whether a real-life problem is deceptive or not. However, there is no information about the relative performance of FUSS on non-deceptive problems. In this paper, the standard selection schemes mentioned above were compared to FUSS on two non-deceptive problems. A GA using FUSS was able to find high-fitness solutions faster than expected. Consequently, FUSS could be a good first-choice selection scheme regardless of whether a problem at hand is deceptive or not.