Injection, Saturation and Feedback in Meta-Heuristic Interactions
Krzysztof Nowak, Dario Izzo, Daniel Hennes · 2015
Meta-heuristics have proven to be an efficient method of handling difficult global optimization tasks. A recent trend in evolutionary computation is the use of several meta-heuristics at the same time, allowing for occasional information exchange among them in hope to take advantage from the best algorithmic properties of all. Such an approach is inherently parallel and, with some restrictions, has a straight forward implementation in a heterogeneous island model. We propose a methodology for characterizing the interplay between different algorithms, and we use it to discuss their performance on real-parameter single objective optimization benchmarks. We introduce the new concepts of feedback, saturation and injection, and show how they are powerful tools to describe the interplay between different algorithms and thus to improve our understanding of the internal mechanism at work in large parallel evolutionary set-ups.