Generalizing surrogate-assisted evolutionary computation

  • Authors:

  • Dudy Lim;Yaochu Jin;Yew-Soon Ong;Bernhard Sendhoff

  • Affiliations:

  • Center for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore;Honda Research Institute Europe GmbH, Offenbach, Germany;Division of Information Systems, School of Computer Engineering, Nanyang Technological University, Singapore and Center for Computational Intelligence, Nanyang Technological University, Singapore;Honda Research Institute Europe GmbH, Offenbach, Germany

  • Venue:
  • IEEE Transactions on Evolutionary Computation
  • Year:
  • 2010

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Abstract

Using surrogate models in evolutionary search provides an efficient means of handling today's complex applications plagued with increasing high-computational needs. Recent surrogate-assisted evolutionary frameworks have relied on the use of a variety of different modeling approaches to approximate the complex problem landscape. From these recent studies, one main research issue is with the choice of modeling scheme used, which has been found to affect the performance of evolutionary search significantly. Given that theoretical knowledge available for making a decision on an approximation model a priori is very much limited, this paper describes a generalization of surrogate-assisted evolutionary frameworks for optimization of problems with objectives and constraints that are computationally expensive to evaluate. The generalized evolutionary framework unifies diverse surrogate models synergistically in the evolutionary search. In particular, it focuses on attaining reliable search performance in the surrogate-assisted evolutionary framework by working on two major issues: 1) to mitigate the 'curse of uncertainty' robustly, and 2) to benefit from the 'bless of uncertainty.' The backbone of the generalized framework is a surrogate-assisted memetic algorithm that conducts simultaneous local searches using ensemble and smoothing surrogate models, with the aims of generating reliable fitness prediction and search improvements simultaneously. Empirical study on commonly used optimization benchmark problems indicates that the generalized framework is capable of attaining reliable, high quality, and efficient performance under a limited computational budget.