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Genetic Programming Theory and Practice VIII pp 129–146Cite as

Age-Fitness Pareto Optimization

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Part of the book series: Genetic and Evolutionary Computation ((GEVO,volume 8))

Abstract

We propose a multi-objective method, inspired by the Age Layered Population Structure algorithm, for avoiding premature convergence in evolutionary algorithms, and demonstrate a three-fold performance improvement over comparable methods. Previous research has shown that partitioning an evolving population into age groups can greatly improve the ability to identify global optima and avoid converging to local optima. Here, we propose that treating age as an explicit optimization criterion can increase performance even further, with fewer algorithm implementation parameters. The proposed method evolves a population on the two-dimensional Pareto front comprising (a) how long the genotype has been in the population (age); and (b) its performance (fitness). We compare this approach with previous approaches on the Symbolic Regression problem, sweeping the problem difficulty over a range of solution complexities and number of variables. Our results indicate that the multi-objective approach identifies the exact target solution more often than the age-layered population and standard population methods. The multi-objective method also performs better on higher complexity problems and higher dimensional datasets - finding global optima with less computational effort.

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Author information

Authors and Affiliations

  1. Computational Biology, Cornell University, 14853, Ithaca, NY, USA

    Michael Schmidt

  2. School of Mechanical and Aerospace Engineering, Cornell University, 14853, Ithaca, NY, USA

    Hod Lipson

  3. Computing and Information Science, Cornell University, 14853, Ithaca, NY, USA

    Hod Lipson

Authors
  1. Michael Schmidt
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  2. Hod Lipson
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Editor information

Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, West Hall 323, Ann Arbor, 48109, Michigan, USA

    Rick Riolo

  2. Solido Design Automation, Inc., Research Drive 102-116, Saskatoon, S7N 3R3, Saskatchewan, Canada

    Trent McConaghy

  3. , Dept. of Mathematics & Computer, University of Antwerp, Building G, Science Campus Middelhe 2020, Antwerpen, Belgium

    Ekaterina Vladislavleva

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Schmidt, M., Lipson, H. (2011). Age-Fitness Pareto Optimization. In: Riolo, R., McConaghy, T., Vladislavleva, E. (eds) Genetic Programming Theory and Practice VIII. Genetic and Evolutionary Computation, vol 8. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7747-2_8

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  • DOI: https://doi.org/10.1007/978-1-4419-7747-2_8

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-7746-5

  • Online ISBN: 978-1-4419-7747-2

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