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. 1 Motivation Almost all practical applications of Evolutionary Algorithms use some sort of hybridisation with other algorithms like heuristics or local searchers, frequently in the form of a Memetic Algorithm (MA)1 . MAs integrate local search in the offspring production part of an EA and, thus, introduce additional strategy parameters controlling the frequency and intensity of the local search among others [2, 3]. The benefit of this app...

• Type: Conference
• Year: 2007
• Where: PPAM
• Authors: Wilfried Jakob
• Keywords: Evolutionary Algorithms, Mathematics, Memetic Algorithm, PPAM 2007, Strategy Parameters