Title :
Objective function decomposition within genetic algorithm
Author :
Khoo, K.G. ; Suganthan, P.N.
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Abstract :
The genetic algorithm (GA) has been applied to numerous optimization problems since its introduction. Here, information on each element of the solution strings is extracted to improve the GA´s performance. We decouple a fitness evaluation function, estimating the fitness contribution by each dimension. Using this information, each dimension within each solution fights for its position in the offspring. A comparison with the standard GA showed that the proposed GA is superior on commonly tested functions
Keywords :
genetic algorithms; fitness evaluation function; genetic algorithm; objective function decomposition; offspring; optimization; performance; solution strings; Data mining; Genetic algorithms; Genetic engineering; Genetic mutations; Genetic programming; Optimization methods; Proposals; Stochastic processes; Testing; Wheels;
Conference_Titel :
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7282-4
DOI :
10.1109/CEC.2002.1006260
