Automated Design of Hybrid Metaheuristics: A Fitness Landscape Analysis

Abstract

The automated design of search techniques is a recent trend in artificial intelligence research. Unfortunately, the majority of the automated design approaches are developed using trial and error which fails to justify or at least explain why some design decisions succeed while others fail. This approach is a host of evils as it has resulted in poorly understood systems for poorly understood problems. This study is an attempt to improve our understanding of the automated design of hybrid metaheuristics by utilizing fitness landscape analysis to reveal the topological characteristics that can be exploited to design better automated approaches. We consider the sequential hybridization, including algorithm configuration and parameter tuning, of single-point and multi-point metaheuristics and three optimization problems which are the earth-observing satellite scheduling problem, the aircraft landing problem and the two-dimensional bin packing problem. Interestingly, the design space exhibits similar trends regardless of the underlining optimization problem. The design space is found to be rugged, multimodal, moderately searchable, has multiple funnels, and almost no plateau. Based on these findings, deeper insights are provided to guide the development of future automated approaches instead of blindly trying different options.