Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evolution Function   [EO]

by

Fukunaga, A., S. and Kahng, A., B.

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Info: 1995 IEEE Conference on Evolutionary Computation (Conference proceedings), 1995, p. 182-187
Keywords:genetic algorithms, genetic programming
Abstract:
Traditional evolutionary optimization algorithms [EO] assume a static environment in which solutions are evolved. Incremental evolution is an approach through which a dynamic evaluation function is scaled over time in order to improve the performance of evolutionary optimization. [EO] In this paper, we present empirical results that demonstrate the effectiveness of this approach for genetic programming. [GP] Using two domains, a two-agent pursuit-evasion game and the Tracker trail-following task, we demonstrate that incremental evolution is most successful when applied near the beginning of an evolutionary run. We also show that incremental evolution can be successful when the intermediate evaluation functions are more difficult than the target evaluation function, as well as they are easier than the target function.
Notes:
ICEC-95 Editors not given by IEEE, Organisers David Fogel and Chris deSilva.
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BibTex:
@InProceedings{fukunaga:1995:dsef,
  author =       "Alex S. Fukunaga and Andrew B. Kahng",
  title =        "Improving the Performance of Evolutionary Optimization
                 by Dynamically Scaling the Evolution Function",
  booktitle =    "1995 IEEE Conference on Evolutionary Computation",
  year =         "1995",
  volume =       "1",
  pages =        "182--187",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www-aig.jpl.nasa.gov/home/fukunaga/publications/ICEC95-priming-camera.ps",
  url_2 =        "http://www.io.org/~causal/c_p/icec95/ec95s106.htm#p0182",
  size =         "6 pages",
  abstract =     "Traditional evolutionary optimization algorithms
                 assume a static environment in which solutions are
                 evolved. Incremental evolution is an approach through
                 which a dynamic evaluation function is scaled over time
                 in order to improve the performance of evolutionary
                 optimization. In this paper, we present empirical
                 results that demonstrate the effectiveness of this
                 approach for genetic programming. Using two domains, a
                 two-agent pursuit-evasion game and the Tracker
                 trail-following task, we demonstrate that incremental
                 evolution is most successful when applied near the
                 beginning of an evolutionary run. We also show that
                 incremental evolution can be successful when the
                 intermediate evaluation functions are more difficult
                 than the target evaluation function, as well as they
                 are easier than the target function.",
  notes =        "ICEC-95 Editors not given by IEEE, Organisers David
                 Fogel and Chris deSilva.

                 ",
}