DocumentCode :
3262800
Title :
GrC model in Genetic Algorithm: Artificial Selection Algorithm
Author :
Chen, Z.H. ; Yan, G.W. ; Xie, G. ; Xie, K.M.
Author_Institution :
Coll. of Inf. Eng., Taiyuan Univ. of Technol., Taiyuan
fYear :
2008
fDate :
26-28 Aug. 2008
Firstpage :
164
Lastpage :
167
Abstract :
Genetic Algorithm (GA), a programming technique that mimics natural evolution as a problem-solving strategy, has become popular since its appearance. It keeps the properties similar to natural selection systems. Many improved GAs has been proposed, however, natural selection essence is not changed. Granular Computing (GrC) brings a new thought for problem solving, it arose a fast growing interests in recent years. This paper build up GrC Model in traditional GA, propose Artificial Selection Algorithm (ASA), which realize intentional reproduction of individuals in a population that have desirable traits. ASA simulates artificial selection behavior of human beings to generate untold diversity in individuals, so as to help guarantee the global convergence and searching efficiency. Artificial selection is a contrast to natural selection, in which the random forces of nature determine becomes the combination of natural selection and artificial selection. The algorithm is validated by benchmark function optimization.
Keywords :
convergence; genetic algorithms; mathematical programming; GrC model; artificial selection algorithm; function optimization; genetic algorithm; global convergence; granular computing; natural evolution; natural selection systems; problem solving strategy; programming technique; searching efficiency; Computational modeling; Convergence; Educational institutions; Genetic algorithms; Genetic programming; Humans; Information systems; Partitioning algorithms; Problem-solving;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing, 2008. GrC 2008. IEEE International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-2512-9
Electronic_ISBN :
978-1-4244-2513-6
Type :
conf
DOI :
10.1109/GRC.2008.4664750
Filename :
4664750
Link To Document :
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