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2019 Evolutionary Algorithms Review

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Genetic Programming Theory and Practice XVII

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

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Abstract

Evolutionary algorithm research and applications began over 50 years ago. Like other artificial intelligence techniques, evolutionary algorithms will likely see increased use and development due to the increased availability of computation, more robust and available open source software libraries, and the increasing demand for artificial intelligence techniques. As these techniques become more adopted and capable, it is the right time to take a perspective of their ability to integrate into society and the human processes they intend to augment. In this review, we explore a new taxonomy of evolutionary algorithms and resulting classifications that look at five main areas: the ability to manage the control of the environment with limiters, the ability to explain and repeat the search process, the ability to understand input and output causality within a solution, the ability to manage algorithm bias due to data or user design, and lastly, the ability to add corrective measures. These areas are motivated by today’s pressures on industry to conform to both societies concerns and new government regulatory rules. As many reviews of evolutionary algorithms exist, after motivating this new taxonomy, we briefly classify a broad range of algorithms and identify areas of future research.

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Acknowledgements

We would like to acknowledge the following people for their encouragement and feedback during the writing of this review, namely Mbou Eyole, Casey Axe, Paul Gleichauf, Gary Carpenter, Andy Loats, Rene De Jong, Charlotte Christopherson, Leonard Mosescu, Vasileios Laganakos, Julian Miller, David Ha, Bill Worzel, William B. Langdon, Daniel Simon, Emre Ozer, Arthur Kordon, Hannah Peeler and Stuart W. Card.

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  1. Arm Inc., Bellevue, WA, USA

    Andrew N. Sloss

  2. MAANA Inc., Bellevue, WA, USA

    Steven Gustafson

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  1. Andrew N. Sloss
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Correspondence to Andrew N. Sloss .

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Editors and Affiliations

  1. Computer Science and Engineering, John R. Koza Chair, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. BEACON Center, Michigan State University, East Lansing, MI, USA

    Erik Goodman

  3. Department of Computer Science and Engineering, Michigan State University, Okemos, MI, USA

    Leigh Sheneman

  4. Depto Ingenieria en Electronic Electrica Tecnológico Nacional de México/ IT, Tijuana, Baja California, Mexico

    Leonardo Trujillo

  5. Evolution Enterprises, Ann Arbor, MI, USA

    Bill Worzel

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Sloss, A.N., Gustafson, S. (2020). 2019 Evolutionary Algorithms Review. In: Banzhaf, W., Goodman, E., Sheneman, L., Trujillo, L., Worzel, B. (eds) Genetic Programming Theory and Practice XVII. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-030-39958-0_16

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  • DOI: https://doi.org/10.1007/978-3-030-39958-0_16

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