Surrogate-assisted evolutionary optimisation: a novel blueprint and a state of the art survey.

Autor: Khaldi, Mohammed Imed Eddine, Draa, Amer
Zdroj: Evolutionary Intelligence; Aug2024, Vol. 17 Issue 4, p2213-2243, 31p
Abstrakt: Surrogate-Assisted Evolutionary Optimisation algorithms are a specialized brand of optimisers developed to undertake problems with computationally expensive fitness functions. These algorithms work by building a cheap approximation or model of the exact function and using it in the evaluation of solutions within the optimisation process. This use of modelling techniques within optimisation, while offers a practical reduction in function calls, brings along with it some additional questions. This paper starts with a description of the key elements of surrogate-assisted evolutionary optimisation algorithms as they are outlined throughout the literature, and then, proceeds to rearrange these elements using a novel blueprint of the field. The proposed blueprint can be used to represent any surrogate-assisted evolutionary algorithm in a way that illustrates its principles and components in a non-vague manner. In addition, a survey of the most prominent works in the field is conducted using this novel blueprint. Finally, a number of challenges and perspectives are listed before the paper is concluded. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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