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The Importance of Proper Diversity Management in Evolutionary Algorithms for Combinatorial Optimization

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NEO 2015

Part of the book series: Studies in Computational Intelligence ((SCI,volume 663))

Abstract

Premature convergence is one of the most important recurrent drawbacks of Evolutionary Algorithms and other metaheuristics. As a result, several methods to alleviate this problem have been devised. One alternative is to explicitly control the diversity of the population. In this chapter, a recently proposed survivor selection strategy is incorporated into a memetic algorithm and analyzed using three different combinatorial optimization problems. This strategy is based on adopting multi-objective concepts for solving single-objective problems by considering the contribution to diversity as an explicit objective. Additionally, it incorporates the principle of adapting the balance between exploration and exploitation to the different stages of the optimization by taking into account the stopping criterion and elapsed time. These new methods provide important benefits when compared to more mature methods that rely on different principles to delay convergence of the population. Additionally, new best-known solutions are generated for several instances of the problems, thus providing proofs of the considerable benefits and robustness yielded by the schemes that incorporate this novel replacement strategy.

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Notes

  1. 1.

    The original website (http://www.sigevo.org/gecco-2008/competitions.html) is not being maintained. We have created a new website from which the evaluator and instances can be downloaded (http://2dpp.cimat.mx).

  2. 2.

    The Sudoku puzzles are available at http://www.cimat.mx/~carlos.segura/Sudoku/SudokuPuzzles.tar.gz.

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Authors and Affiliations

  1. Centro de Investigación en Matemáticas, A.C. (CIMAT), Área de Computación, Jalisco S/N, Col. Valenciana, 36023, Guanajuato, Mexico

    Carlos Segura, Arturo Hernández Aguirre, Sergio Ivvan Valdez Peña & Salvador Botello Rionda

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  1. Carlos Segura
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  2. Arturo Hernández Aguirre
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  3. Sergio Ivvan Valdez Peña
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  4. Salvador Botello Rionda
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Correspondence to Carlos Segura .

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Editors and Affiliations

  1. Departamento de Comput Cinvestav-IPN, Mexico, Mexico

    Oliver Schütze

  2. Posgrado en Ciencias de la Ingeniería, Instituto Tecnológico de Tijuana, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  3. Université de Bordeaux, Bordeaux, France

    Pierrick Legrand

  4. Posgrado en Ciencias de la Ingeniería, Instituto Tecnológico de Tijuana, Tijuana, Mexico

    Yazmin Maldonado

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Segura, C., Aguirre, A.H., Peña, S.I.V., Rionda, S.B. (2017). The Importance of Proper Diversity Management in Evolutionary Algorithms for Combinatorial Optimization. In: Schütze, O., Trujillo, L., Legrand, P., Maldonado, Y. (eds) NEO 2015. Studies in Computational Intelligence, vol 663. Springer, Cham. https://doi.org/10.1007/978-3-319-44003-3_6

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