Optimizing neural networks with genetic algorithms

Steven A. Harp, Tario Samad

Research output: Contribution to journalConference articlepeer-review

16 Scopus citations


We describe an approach to application-specific neural network design using genetic algorithms. A genetic algorithm is a robust optimization method particularly well suited for search spaces that are high-dimensional, discontinuous and noisy - features that typify the neural network design problem. Our approach is relevant to virtually all neural network applications: it is network-model independent and it permits optimization for arbitrary, user-defined criteria. We have developed an experimental system, NeuroGENESYS, and have conducted several experiments on small-scale problems. Performance improvements over manual designs have been observed, the interplay between performance criteria and network design aspects has been demonstrated, and general design principles have been uncovered.

Original languageEnglish (US)
Pages (from-to)1138-1143
Number of pages6
JournalProceedings of the American Power Conference
Issue numberpt 2
StatePublished - Dec 1 1992


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