Control of numeric and symbolic parameters with a hybrid scheme based on fuzzy logic and hyper-heuristics

One of the main disadvantages of Evolutionary Algorithms (EAs) is that they converge towards local optima for some problems. In recent years, diversity-based multi-objective EAs have emerged as a promising technique to prevent from local optima stagnation when optimising single-objective problems. An additional drawback of EAs is the large dependency between the quality of the results provided and the setting of their parameters. By the use of parameter control methods, parameter values can be adapted during the run of an EA. The aim of control approaches is not only to improve the robustness of the controlled algorithm, but also to boost its efficiency. In this paper we apply a novel hybrid parameter control scheme based on Fuzzy Logic and Hyper-heuristics to simultaneously adapt several numeric and symbolic parameters of a diversity-based multi-objective EA. An extensive experimental evaluation is carried out, which includes a comparison between the hybrid control proposal and a wide range of configurations of the diversity-based multi-objective EA with fixed parameters. Results demonstrate that our control proposal is able to find similar or even better solutions than those obtained by the best configuration of the diversity-based scheme with fixed parameters in a significant number of benchmark problems, demonstrating the advantages of parameter control over parameter tuning for these test cases.