A general cost-benefit-based adaptation framework for multimeme algorithms

As memetic algorithms (MA) are a crossbreed between local searchers and evolutionary algorithms (EA) spreading of computational resources between evolutionary and local search is a key issue for a good performance, if not for success at all. This paper summarises and continues previous work on a general cost-benefit-based adaptation scheme for the choice of local searchers (memes), the frequency of their usage, and their search depth. This scheme eliminates the MA strategy parameters controlling meme usage, but raises new ones for steering the adaptation itself. Their impact is analysed and it will be shown that in the end the number of strategy parameters is decreased significantly as well as their range of meaningful values. In addition to this the number of fitness evaluations is reduced drastically. Both are necessary prerequisites for many practical applications as well as for the acceptance of the method by practitioners. Although the introduced framework is tailored to EAs producing more than one offspring per mating, it is also suited for those with only one child per pairing. So there are no preconditions to the EA for the described adaptation scheme to be applied.