Does Comma Selection Help To Cope With Local Optima
This provides a theoretical result for evolutionary computation researchers, showing that non-elitism does not improve runtime on basic multimodal problems, which is incremental as it builds on prior runtime analysis.
The paper tackled whether non-elitist evolutionary algorithms help escape local optima by analyzing the runtime of the $(\\mu,\\lambda)$ EA on a jump function, finding that its expected runtime is at least as large as the elitist $(\\mu+\\lambda)$ EA, showing no advantage.
One hope when using non-elitism in evolutionary computation is that the ability to abandon the current-best solution aids leaving local optima. To improve our understanding of this mechanism, we perform a rigorous runtime analysis of a basic non-elitist evolutionary algorithm (EA), the $(μ,λ)$ EA, on the most basic benchmark function with a local optimum, the jump function. We prove that for all reasonable values of the parameters and the problem, the expected runtime of the $(μ,λ)$~EA is, apart from lower order terms, at least as large as the expected runtime of its elitist counterpart, the $(μ+λ)$~EA (for which we conduct the first runtime analysis on jump functions to allow this comparison). Consequently, the ability of the $(μ,λ)$~EA to leave local optima to inferior solutions does not lead to a runtime advantage. We complement this lower bound with an upper bound that, for broad ranges of the parameters, is identical to our lower bound apart from lower order terms. This is the first runtime result for a non-elitist algorithm on a multi-modal problem that is tight apart from lower order terms.