Numerical optimization with neuroevolution

Brian Greer, Henri Hakonen, Risto Lahdelma, Risto Miikkulainen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

15 Citations (Scopus)


Neuroevolution techniques have been successful in many sequential decision tasks, such as robot control and game playing. This paper aims at establishing whether they can be useful in numerical optimization more generally, by comparing neuroevolution to linear programming in a manufacturing optimization domain. It turns out that neuroevolution can learn to compensate for uncertainty in the data and outperform linear programming when the number of variables in the problem is small and the required precision is low, but the current techniques do not (yet) provide an advantage in problems where many variables must be optimized with high precision.

Original languageEnglish
Title of host publicationProceedings of the 2002 Congress on Evolutionary Computation, CEC 2002
Number of pages6
ISBN (Print)0-7803-7282-4
Publication statusPublished - 2002
MoE publication typeA4 Conference publication
EventIEEE Congress on Evolutionary Computation - Honolulu, United States
Duration: 12 May 200217 May 2002


ConferenceIEEE Congress on Evolutionary Computation
Abbreviated titleCEC
Country/TerritoryUnited States


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