A COMPREHENSIVE ANALYSIS OF MULTI-STRATEGY MEMETIC ALGORITHMS INCORPORATING LOW-LEVEL HEURISTICS AND ACCEPTANCE MECHANISMS

Abstract

Hyper-heuristics are designed to be reusable, domain-independent methods for addressing complex computational issues. While there are specialized approaches that work well for particular problems, they often require parameter tuning and cannot be transferred to other problems. Memetic Algorithms combine genetic algorithms and local search techniques. The evolutionary interaction of memes allows for the creation of intelligent complexes capable of solving computational problems. Hyper-heuristics are a high-level search technique that operates on a set of low-level heuristics that directly address the solution. They have two main components: heuristic selection and move acceptance mechanisms. The heuristic selection method determines which low-level heuristic to use, while the move acceptance mechanism decides whether to accept or reject the resulting solution. In this study, we explore a multi-meme memetic algorithm as a hyper-heuristic that integrates and manages multiple hyper-heuristics (Modified Choice Function All Moves, Reinforcement Learning with Great Deluge, and Simple Random Only Improvement) and parameters of heuristics (such as mutation rates and search depth). We conducted an empirical study testing two different variations of the proposed hyper-heuristic. The first algorithm uses the Only Improvement acceptance technique for both Reinforcement Learning and Simple Random, and All Moves for Modified Choice Function. In the second version, the Great Deluge method replaces Only Improvement for Reinforcement Learning. The second algorithm's results were the best of all competitors from the CHeSC2011 competition, achieving the fourth-best hyper-heuristic performance.