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Trusted evolutionary algorithm

Ong, Y-S, Jin, Y, Sendhoff, B, Lim, D, Ong, Y-S, Jin, Y and Sendhoff, B (2006) Trusted evolutionary algorithm

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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. © 2006 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
Ong, Y-S
Jin, Y
Sendhoff, B
Lim, D
Ong, Y-S
Jin, Y
Sendhoff, B
Date : 2006
DOI : 10.1109/CEC.2006.1688302
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:42
Last Modified : 31 Oct 2017 14:35

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