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Trusted Evolutionary Algorithm

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4 Author(s)
Dudy Lim ; Emerging Research Lab, School of Computer Engineering, Nanyang Technological University, Blk N4, B3b-06, Nanyang Avenue, Singapore 639798 (e-mail: ; Yew-Soon Ong ; Yaochu Jin ; B. Sendhoff

In both numerical and stochastic optimization methods, surrogate models are often employed in lieu of the expensive high-fidelity models to enhance search efficiency. In gradient-based numerical methods, the trustworthiness of the surrogate models in predicting the fitness improvement is often addressed using ad hoc move limits or a trust region framework (TRF). Inspired by the success of TRF in line search, here we present a Trusted Evolutionary Algorithm (TEA) which is a surrogate-assisted evolutionary algorithm that exhibits the concept of surrogate model trustworthiness in its search. Empirical study on benchmark functions reveals that TEA converges to near-optimum solutions more efficiently than the canonical evolutionary algorithm.

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2006 IEEE International Conference on Evolutionary Computation

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