An empirical study of impact of crossover operators on the performance of non-binary genetic algorithm based neural approaches for classification

Parag C. Pendharkar, James A. Rodger

Research output: Contribution to journalArticle

45 Citations (Scopus)

Abstract

We study the performance of genetic algorithm (GA) based artificial neural network (ANN) for different crossover operators. We use simulated and real life data to test the performance of GA-based ANN. Our results indicate that arithmetic crossover operator may be a suitable crossover operator for GA based ANN. Genetic algorithm based artificial neural networks are used in several classification and forecasting applications. Among several genetic algorithm design operators, crossover plays an important role for convergence to the global heuristic solution. Several crossover operators exist, and selection of a crossover operator is an important design issue confronted by most researchers. The current study investigates the impact of different crossover operators on the performance of genetic algorithm based artificial neural networks.

Original languageEnglish (US)
Pages (from-to)481-498
Number of pages18
JournalComputers and Operations Research
Volume31
Issue number4
DOIs
StatePublished - Jan 1 2004

Fingerprint

Crossover Operator
Empirical Study
Mathematical operators
Genetic algorithms
Genetic Algorithm
Artificial Neural Network
Neural networks
Algorithm Design
Empirical study
Operator
Crossover
Genetic algorithm
Forecasting
Artificial neural network
Heuristics

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modeling and Simulation
  • Management Science and Operations Research

Cite this

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