A cost-benefit-based adaptation scheme for multimeme algorithms

Abstract

Memetic Algorithms are the most frequently used hybrid of Evolutionary Algorithms (EA) for real-world applications. This paper will deal with one of the most important obstacles to their wide usage: compared to pure EA, the number of strategy parameters which have to be adjusted properly is increased. A cost-benefit-based adaptation scheme suited for every EA will be introduced, which leaves only one strategy parameter to the user, the population size. Furthermore, it will be shown that the range of feasible sizes can be reduced drastically. © 2008 Springer-Verlag Berlin Heidelberg.