Representing experience in continuous evolutionary optimisation through problem-tailored search operators

Evolutionary algorithms are a class of population-based meta-heuristic methods partially inspired by natural evolution. Specifically, they rely on stochastic variation and selection processes to sequentially find optimal solutions of a function of interest. We attempt in this work to extract preferences in these stochastic evolutionary operators in form of empirical and improved distributions as basis for model-based mutation operators. The latter can be considered as representing problem-tailored search operators which exist independently from the optimisation run and thus can be transferred to similar problem instances. This offline approach is different to existing model-based optimisation techniques, e.g. EDA's, CMA-ES and Bayesian approaches, where adaption happens rather in an online manner without the influence of prior experience. Our approach can be rather considered to follow the recent line of research on knowledge transfer in optimisation, which until now heavily relies upon the transfer of candidate solutions across different optimisation tasks. We investigate in this paper the interplay between algorithm and optimisation task, its influence on the retrieved distributions and explore whether or not these can lead to performance improvements on a selected range of problems, as well as when transferring them across problems. At last, we make a comparison of built distributions in the hope of relating similarity in statistical distances between distributions to possible performance gains.