On the analysis of a (1+1) adaptive memetic algorithm

A memetic algorithm is an evolutionary algorithm augmented with a local search. For many applications, researchers have applied variations of memetic algorithms and have gained very positive experimental results. But the theory of these variations of memetic algorithms is still underdeveloped. This paper defines the (1+1) adaptive memetic algorithm with a dynamic mutation probability, and analyzes two types of local searches. We then propose different classes of functions for studying the performance of evolutionary algorithms. We give time complexity analysis that proves our two local searches can outperform each other on different functions. Also we show that memetic algorithms with dynamic mutation probabilities can out-perform memetic algorithms with static mutation probabilities, and vice versa.