Experimental validations of the learnable evolution model

Guido Cervone, Kenneth K. Kaufman, Ryszard S. Michalski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

A recently developed approach to evolutionary computation, called Learnable Evolution Model or LEM, employs machine learning to guide processes of generating new populations. The central new idea of LEM is that it generates new individuals not by mutation and/or recombination, but by processes of hypothesis generation and instantiation. The hypotheses are generated by a machine learning system from examples of high and low performance individuals. When applied to problems of function optimization and parameter estimation for nonlinear filters, LEM significantly outperformed the standard evolutionary computation algorithms used in experiments, sometimes achieving two or more orders of magnitude of evolutionary speed-up (in terms of the number of births). An application of LEM to the problem of optimizing heat exchangers has produced designs equal to or exceeding the best human designs. Further research needs to explore trade-offs and determine best areas for LEM application.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Evolutionary Computation, ICEC
Pages1064-1071
Number of pages8
Volume2
StatePublished - 2000
EventProceedings of the 2000 Congress on Evolutionary Computation - California, CA, USA
Duration: Jul 16 2000Jul 19 2000

Other

OtherProceedings of the 2000 Congress on Evolutionary Computation
CityCalifornia, CA, USA
Period7/16/007/19/00

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science(all)
  • Computational Theory and Mathematics

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