Surrogate-assisted evolutionary computation: Recent advances and future challenges

Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single- or multi-objective optimization problems, but also in addressing dynamic optimization problems, constrained optimization problems and multi-modal optimization problems. This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.

► We review the state-of-the art of surrogate-assisted evolutionary computation.
► We focus on recent advancements in managing surrogates for efficient evolutionary optimization of expensive problems.
► New applications of surrogates to constrained optimization and dynamic optimization are also highlighted.
► Future promising research topics are suggested.