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Heuristic Search Strategies for Noisy Optimization

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Learning and Intelligent Optimization (LION 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12096))

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Abstract

Many real-world optimization problems are subject to noise, and making correct comparisons between candidate solutions is not straightforward. In the literature, various heuristics have been proposed to deal with this problem. Most studies compare evolutionary strategies with algorithms which propose candidate solutions deterministically. This paper compares the efficiency of different randomized heuristic search strategies, and also extends randomized algorithms non based on populations with a statistical analysis technique in order to deal with the presence of noise. Results show that this extension can outperform population-based algorithms, especially with higher levels of noise.

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Notes

  1. 1.

    https://github.com/beniz/libcmaes.

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Authors and Affiliations

  1. Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123, Povo, TN, Italy

    Manuel Dalcastagné

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  1. Manuel Dalcastagné
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Correspondence to Manuel Dalcastagné .

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Editors and Affiliations

  1. CARGO Lab., Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias S. Kotsireas

  2. Center for Applied Optimization, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Dalcastagné, M. (2020). Heuristic Search Strategies for Noisy Optimization. In: Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2020. Lecture Notes in Computer Science(), vol 12096. Springer, Cham. https://doi.org/10.1007/978-3-030-53552-0_32

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