Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing

DOI10.1016/S0377-2217(98)00114-3zbMath0938.90069OpenAlexW1996688253MaRDI QIDQ1124721

Robert E. Dorsey, John D. Johnson, Randall S. Sexton

Publication date: 25 November 1999

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0377-2217(98)00114-3

zbMATH Keywords

Monte Carlo studyneural networkssimulated annealinggenetic algorithmglobal solutions

Mathematics Subject Classification ID

Programming involving graphs or networks (90C35) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (9)

Global optimization issues in deep network regression: an overview ⋮ A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem ⋮ TRANSFORM-ANN for online optimization of complex industrial processes: casting process as case study ⋮ An empirical study of impact of crossover operators on the performance of non-binary genetic algorithm based neural approaches for classification. ⋮ An investigation of neural networks for linear time-series forecasting ⋮ COMPARISON OF SCHEDULING EFFICIENCY IN TWO/THREE-MACHINE NO-WAIT FLOW SHOP PROBLEM USING SIMULATED ANNEALING AND GENETIC ALGORITHM ⋮ A review of Hopfield neural networks for solving mathematical programming problems ⋮ Genetic algorithms in logic tree decision modeling ⋮ A computational study on the performance of artificial neural networks under changing structural design and data distribution

Uses Software

• simannf90

Cites Work

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• Global optimization for artificial neural networks: A tabu search application
• Multilayer feedforward networks are universal approximators
• Learning Process in an Asymmetric Threshold Network
• Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm—Corrigenda for this article is available here
• Mathematical Programming in Neural Networks
• A GRG2-Based System for Training Neural Networks: Design and Computational Experience
• The Unpredictability of Standard Back Propagation Neural Networks in Classification Applications
• Breast Cancer Diagnosis and Prognosis Via Linear Programming
• Learning representations by back-propagating errors

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