Memetic algorithm using multi-surrogates for computationally expensive optimization problems

Authors:
Zongzhao Zhou;Yew Soon Ong;Meng Hiot Lim;Bu Sung Lee
Affiliations:
Nanyang Technological University, School of Computer Engineering, 639798, Singapore, Singapore;Nanyang Technological University, School of Computer Engineering, 639798, Singapore, Singapore;Nanyang Technological University, School of Electrical and Electronic Engineering, Block S1, 639798, Singapore, Singapore;Nanyang Technological University, School of Computer Engineering, 639798, Singapore, Singapore
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2007

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Abstract

In this paper, we present a multi-surrogates assisted memetic algorithm for solving optimization problems with computationally expensive fitness functions. The essential backbone of our framework is an evolutionary algorithm coupled with a local search solver that employs multi-surrogate in the spirit of Lamarckian learning. Inspired by the notion of ‘blessing and curse of uncertainty’ in approximation models, we combine regression and exact interpolating surrogate models in the evolutionary search. Empirical results are presented for a series of commonly used benchmark problems to demonstrate that the proposed framework converges to good solution quality more efficiently than the standard genetic algorithm, memetic algorithm and surrogate-assisted memetic algorithms.