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

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Abstract

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.

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Notes

  1. The purpose given here is just an example, the approximation action can have other purposes which will be discussed later.

  2. We include in our denotation of evolutionary algorithms here swarm intelligence algorithms.

  3. PFoM refers to methods that combine predicted fitness and uncertainty estimates in a different manner than the three first criteria.

  4. The selection of sample points is not to be confused with the evolutionary selection operator of the EA.

  5. This question works better if we say what to optimise?

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  1. MISC Laboratory, Constantine 2 University- Abdelhamid Mehri, Campus Ali Mendjli, 25000, Constantine, Algeria

    Mohammed Imed Eddine Khaldi

  2. NTIC Faculty, Constantine 2 University - Abdelhamid Mehri, Campus Ali Mendjli, 25000, Constantine, Algeria

    Amer Draa

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Khaldi, M.I.E., Draa, A. Surrogate-assisted evolutionary optimisation: a novel blueprint and a state of the art survey. Evol. Intel. 17, 2213–2243 (2024). https://doi.org/10.1007/s12065-023-00882-8

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