Memetic Algorithm and Evolutionary Operators for Multi-Objective Matrix Tri-Factorization Problem

Abstract

In memetic algorithm, a population based global search technique is used to broadly locate good areas of the search space, while repeated usage of a local search heuristic is employed to locate optimum. Intuitively, evolutionary operators that generate individuals with genetic material inherited from the parents and improved performance ability should be the right option for improved performance of the algorithm in terms of time and solution quality. Evolutionary operators with such properties were devised and used in memetic algorithm for solving multi-objective matrix tri-factorization problem. It was shown, by comparing deterministic naive approach with two variants of memetic algorithm with different level of inheritance, that evolutionary operators do not improve performance in this case. Further analysis showed that even though proposed evolutionary operators inherit high fitness from its parents, local search does not perform well on such offspring which results in poor performance.