DocumentCode
1684652
Title
Optimization with neural networks trained by evolutionary algorithms
Author
Velazco, Marta I. ; Lyra, Christiano
Author_Institution
Sch. of Electr. Eng. & Comput. Sci., State Univ. of Campinas, Brazil
Volume
2
fYear
2002
fDate
6/24/1905 12:00:00 AM
Firstpage
1516
Lastpage
1521
Abstract
Multilayer neural networks are trained to solve optimization problems. Genetic algorithms are adopted to "evolve" weights, unveiling new points in the definition domain. As the evolution goes on, better points are found, driving the process to optimality. Case studies with convex and nonconvex problems illustrate the approach
Keywords
concave programming; convex programming; feedforward neural nets; genetic algorithms; learning (artificial intelligence); mathematics computing; case studies; convex problems; definition domain; evolutionary algorithms; genetic algorithms; multilayer neural network training; nonconvex problems; optimization problems; weight evolution; Computer science; Evolutionary computation; Genetic algorithms; Hopfield neural networks; Logic; Merging; Multi-layer neural network; Network topology; Neural networks; Neurons;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location
Honolulu, HI
ISSN
1098-7576
Print_ISBN
0-7803-7278-6
Type
conf
DOI
10.1109/IJCNN.2002.1007742
Filename
1007742
Link To Document