Hybridizing Genetic Algorithms with Pareto Local Search for Multi-Objective Rule Learning

Abstract

Rule-based classifiers offer high interpretability, which is essential in domains such as medicine where decision transparency is key. However, multi-objective rule learning remains a complex challenge due to the highly combinatorial nature of the search space. This work investigates the hybridization of a Genetic Algorithm (GA) with Pareto Local Search (PLS) to improve the exploration of non-dominated rulesets. Built upon the MH-Builder meta-heuristics framework, and the existing Multi-Objective Classification Algorithm for Imbalanced Data, or MOCA-I, our proposed pipeline uses a GA phase for global exploration followed by an Iterated PLS phase for intensification. Our results demonstrate that while the hybrid MOCA-I+GA pipeline maintains the same high predictive performance as MOCA-I, the evolutionary phase provides only marginal improvements in diversity (Hypervolume and Spread) at a significant computational cost. This study provides a documented baseline for hybrid metaheuristics in rule mining and discusses the limitations of loose-coupled sequential hybridization.