DocumentCode :
1747774
Title :
Multi-objective evolutionary algorithm with non-stationary search space
Author :
Khor, E.F. ; Tan, K.C. ; Lee, T.H.
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore
Volume :
1
fYear :
2001
fDate :
2001
Firstpage :
527
Abstract :
Existing multi-objective (MO) evolutionary algorithms apply a fixed search space in the parameter domain. This approach needs a good guess or a-prior knowledge of a promising search area since a wrongly specified range of search space often leads to poor solutions. To address the issue, this paper proposes a novel approach of adaptive search space for MO optimization. Through the method of shrinking and expanding, the technique is capable of directing the evolution to reach more promising search regions even if it is not covered in the initial search space. The role of the inductive learning process is also introduced, which is performed by an exploratory multi-objective evolutionary algorithm to enhance the search from being trapped in local optima as well as to promote the population diversity along the discovered Pareto-optimal front. Features of the proposed approach are experimented and investigated upon benchmark MO optimization problems
Keywords :
evolutionary computation; learning by example; search problems; Pareto-optimal front; adaptive search space; inductive learning; multi-objective evolutionary algorithm; nonstationary search space; optimization; population diversity; Algorithm design and analysis; Biological cells; Biological systems; Design optimization; Evolution (biology); Evolutionary computation; Genetic algorithms; Machine learning; Optimization methods; Timing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2001. Proceedings of the 2001 Congress on
Conference_Location :
Seoul
Print_ISBN :
0-7803-6657-3
Type :
conf
DOI :
10.1109/CEC.2001.934437
Filename :
934437
Link To Document :
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