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Economic Modeling Using Evolutionary Algorithms: The Influence of Mutation on the Premature Convergence Effect


  • Michael Maschek


This work is concerned with the possible impact binary encoding of strategies may have on the performance of genetic algorithms popular in agent-based computational economic research. In their recent work, Waltman et al. (J Evol Econ 21(5): 737–756, 2011 ) consider binary encoding and its possible contribution to a phenomenon referred to as premature convergence; the observation that different individual runs of the genetic algorithm can lead to very different results. While Alkemade et al. (Comput Econ 28(4): 355–370, 2006 ), (Comput Intell 23(2): 162–175, 2007 ), (Comput Econ 33(1): 99–101, 2009 ) argue that premature convergence is caused by insufficient population size, Waltman et al. argue that this phenomenon depends crucially on strategies being encoded in binary form. This conclusion is based on their illustration that premature convergence can be avoided even in simulations with small populations so long as real, rather than binary, encoding of strategies is utilized. Utilizing their methodology, we return to the consideration of the cause of premature convergence. After robustness checks with respect to the length of the binary string used for encoding, the fitness function, and the form of mutation, it is concluded that an alternative specification of mutation may also alleviate the occurrence of premature convergence. It is argued that this alternative form of mutation may be more appropriate in a wider range of problems where real encoding of strategies may not prove sufficient. Copyright Springer Science+Business Media New York 2016

Suggested Citation

  • Michael Maschek, 2016. "Economic Modeling Using Evolutionary Algorithms: The Influence of Mutation on the Premature Convergence Effect," Computational Economics, Springer;Society for Computational Economics, vol. 47(2), pages 297-319, February.
  • Handle: RePEc:kap:compec:v:47:y:2016:i:2:p:297-319
    DOI: 10.1007/s10614-015-9485-8

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    Cited by:

    1. Adeola Oyenubi, 2019. "Diversification Measures and the Optimal Number of Stocks in a Portfolio: An Information Theoretic Explanation," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1443-1471, December.

    More about this item


    Agent-based computational economics; Evolutionary algorithm; Genetic algorithm; Premature convergence; C63; C73; D43; D83;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness


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