An Adaptive Hybrid Genetic Algorithm - Simulated Annealing Approach

Manar Hosny

ISBN 10: 3838335295 / ISBN 13: 9783838335292

Published by LAP LAMBERT Academic Publishing Dez 2009, 2009

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Genetic algorithms (GAs) and simulated annealing (SA) are important search methods. Combining both may improve the search quality, for example by using SA as a genetic operator. One problem in such technique is to find annealing parameters that work for all stages of the run. In this research, we introduce a new adaptive hybrid GA-SA algorithm, in which SA acts as a mutation. However, the SA will be adaptive in the sense that its parameters are evolved during the search. Adaptation should help guide the search towards optimum solutions with minimum parameter tuning. The algorithm is tested on solving an important NP-hard problem, the MAP (Maximum a-Posteriori) Assignment Problem on BBNs (Bayesian Belief Networks). The results obtained indicate that the adaptive hybrid algorithm provides an improvement of solution quality over that obtained by GA used alone and GA augmented with standard non-adaptive SA. Its effect, however, is more profound for large problems, which are difficult for GA alone to solve. The techniques reported in this book should be of interest to researchers in heuristics and meta-heuristics, and their application to combinatorial optimization problems.Books on Demand GmbH, Überseering 33, 22297 Hamburg 176 pp. Englisch.

Seller Inventory # 9783838335292

Bibliographic Details

• Title: An Adaptive Hybrid Genetic Algorithm - Simulated Annealing Approach
• Publisher: LAP LAMBERT Academic Publishing Dez 2009
• Publication Date: 2009
• Language: English
• ISBN 10: 3838335295
• ISBN 13: 9783838335292
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 12D millimeters 220 millimeters width by 150 millimeters height by 12 millimeters depth
• Weight: 280 grams

About this title

Synopsis

Genetic algorithms (GAs) and simulated annealing (SA) are important search methods. Combining both may improve the search quality, for example by using SA as a genetic operator. One problem in such technique is to find annealing parameters that work for all stages of the run. In this research, we introduce a new adaptive hybrid GA-SA algorithm, in which SA acts as a mutation. However, the SA will be adaptive in the sense that its parameters are evolved during the search. Adaptation should help guide the search towards optimum solutions with minimum parameter tuning. The algorithm is tested on solving an important NP-hard problem, the MAP (Maximum a-Posteriori) Assignment Problem on BBNs (Bayesian Belief Networks). The results obtained indicate that the adaptive hybrid algorithm provides an improvement of solution quality over that obtained by GA used alone and GA augmented with standard non-adaptive SA. Its effect, however, is more profound for large problems, which are difficult for GA alone to solve. The techniques reported in this book should be of interest to researchers in heuristics and meta-heuristics, and their application to combinatorial optimization problems.