The effect of preadaptation epoch length on performance in an exaptive genetic algorithm

Authors:
K. J. Lee Graham;Robert Cattral;Franz Oppacher
Affiliations:
School of Computer Science, Carleton University, Ottawa, Ontario, Canada;Computer Science, Carleton University, Ottawa, Ontario, Canada;School of Computer Science, Carleton University, Ottawa, Ontario, Canada
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

We explore a simple genetic algorithm (GA) in which two different fitness functions are combined and used together in an epoch of preadaptation prior to an epoch involving only one of the fitness functions. The effects of preadaptation epoch length on mean best-of-run fitness and success rate statistics are examined and contrasted with those of an otherwise identical GA using no preadaptation. The results show that, for this problem at least, the right amount of preadaptation can be very beneficial, and that both too much and too little preadaptation can be detrimental (as opposed to merely less beneficial).