Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation
This work provides theoretical runtime guarantees for niching methods in evolutionary algorithms, addressing multimodal optimization problems relevant to real-world applications, but it is incremental as it focuses on specific methods and a benchmark function.
The paper tackled the problem of identifying multiple optima in multimodal optimization by analyzing two niching methods, probabilistic crowding and restricted tournament selection (RTS), on the bimodal function Twomax. It found that probabilistic crowding fails to find good solutions even in exponential time, while RTS succeeds in finding both optima in O(μn log n) time with high probability when the window size is sufficiently large.
Many real optimisation problems lead to multimodal domains and so require the identification of multiple optima. Niching methods have been developed to maintain the population diversity, to investigate many peaks in parallel and to reduce the effect of genetic drift. Using rigorous runtime analysis, we analyse for the first time two well known niching methods: probabilistic crowding and restricted tournament selection (RTS). We incorporate both methods into a $(μ+1)~EA$ on the bimodal function Twomax where the goal is to find two optima at opposite ends of the search space. In probabilistic crowding, the offspring compete with their parents and the survivor is chosen proportionally to its fitness. On Twomax probabilistic crowding fails to find any reasonable solution quality even in exponential time. In RTS the offspring compete against the closest individual amongst $w$ (window size) individuals. We prove that RTS fails if $w$ is too small, leading to exponential times with high probability. However, if w is chosen large enough, it finds both optima for Twomax in time $O(μn \log{n})$ with high probability. Our theoretical results are accompanied by experimental studies that match the theoretical results and also shed light on parameters not covered by the theoretical results.