DocumentCode :
2027781
Title :
An investigation of the use of local search in NP-hard problems
Author :
Newth, David ; Kirley, Michael ; Green, David G.
Author_Institution :
Sch. of Environ. & Inf. Sci., Charles Sturt Univ., Albury, NSW, Australia
Volume :
4
fYear :
2000
fDate :
2000
Firstpage :
2710
Abstract :
We combine local search algorithms with genetic algorithms. In this context local search can be thought of as learning over an individual´s lifetime. We investigate two different ways of incorporating learning into the hybrid algorithm: Lamarckian evolution and the Baldwin effect. For each model we systematically vary the proportion of the population undergoing learning. We found that the quality of solution improves significantly at or above a critical level of learning
Keywords :
genetic algorithms; learning (artificial intelligence); search problems; Baldwin effect; Lamarckian evolution; genetic algorithms; learning; local search algorithms; Australia; Biological system modeling; Evolution (biology); Evolutionary computation; Genetic algorithms; NP-hard problem; Optimization methods; Search methods; Simulated annealing; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE
Conference_Location :
Nagoya
Print_ISBN :
0-7803-6456-2
Type :
conf
DOI :
10.1109/IECON.2000.972426
Filename :
972426
Link To Document :
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