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Why Convex Combination is an Effective Crossover Operation in Continuous Optimization: A Theoretical Explanation

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Uncertainty, Constraints, and Decision Making

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 484))

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Abstract

When evolutionary computation techniques are used to solve continuous optimization problems, usually, convex combination is used as a crossover operation. Empirically, this crossover operation works well, but this success is, from the foundational viewpoint, a challenge: why this crossover operation works well is not clear. In this paper, we provide a theoretical explanation for this empirical success.

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References

  1. M. Castelli, L. Manzoni, GSGP-C++ 2.0: a geometric semantic genetic programming framework. SoftwareX, vol. 10, paper 100313 (2019)

    Google Scholar 

  2. K. Krawiec, T. Pawlak, Locally geometric semantic crossover: a study on the roles of semantics and homology in recombination operators. Gen. Program. Evol. Mach. 14, 31–63 (2013)

    Article  Google Scholar 

  3. A. Moraglio, K. Krawiec, C.G. Johnson, Geometric semantic genetic programming, in Parallel Problem Solving from Nature PPSN’XII (Springer, Heidelberg, Germany, 2012), pp. 21–31

    Google Scholar 

  4. T.P. Pawlak, B. Wieloch, K. Krawiec, Review and comparative analysis of geometric semantic crossovers. Gen. Program. Evol. Mach. 16, 351–386 (2015)

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the National Science Foundation grants 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science), and HRD-1834620 and HRD-2034030 (CAHSI Includes), and by the AT&T Fellowship in Information Technology.

It was also supported by the program of the development of the Scientific-Educational Mathematical Center of Volga Federal District No. 075-02-2020-1478, and by a grant from the Hungarian National Research, Development and Innovation Office (NRDI).

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Authors and Affiliations

  1. Department of Aerospace Engineering and Engineering Mechanics, College of Engineering and Applied Science, University of Cincinnati, 2850 Campus Way, Cincinnati, OH, 45221-0070, USA

    Kelly Cohen

  2. University of Texas at El Paso, 500 W. University, El Paso, TX, 79968, USA

    Olga Kosheleva & Vladik Kreinovich

Authors
  1. Kelly Cohen
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  2. Olga Kosheleva
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  3. Vladik Kreinovich
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Corresponding author

Correspondence to Vladik Kreinovich .

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Editors and Affiliations

  1. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Martine Ceberio

  2. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

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Cohen, K., Kosheleva, O., Kreinovich, V. (2023). Why Convex Combination is an Effective Crossover Operation in Continuous Optimization: A Theoretical Explanation. In: Ceberio, M., Kreinovich, V. (eds) Uncertainty, Constraints, and Decision Making. Studies in Systems, Decision and Control, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-031-36394-8_54

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