Paper Info
Title
Robust Evolutionary Algorithms With Toroidal Search Space Conversion For Function Optimization
Abstract
This paper presents a new method that improves robustness of Real-Coded Evolu- tionary Algorithms (RCEAs), such as Real- Coded Genetic Algorithms and Evolution Strategies, for function optimization. It is reported that most crossover (or recombina- tion) operators for RCEAs has sampling bias that prevents to find the optimum near the boundary of search space. They like to search the center of search space much more than the other. Therefore, they will not work on functions that have their optima near the boundary of the search space. Although sev- eral methods have been proposed to reduce this sampling bias, they could not cancel the whole bias. In this paper, we propose a new method, Toroidal Search Space Conversion (TSC), to remove this sampling bias. TSC converts bounded search space into toroidal one with no parameters. Experimental re- sults show that a RCEA with TSC has higher performance to find the optimum near the boundary of search space and it has improved robustness concerning the relative position of the optimum.
Year
Venue
Keywords
2002
GECCO
robust evolutionary algorithms,toroidal search space conversion,function optimization,evolutionary algorithm,evolution strategy,search space
Field
DocType
ISBN
Mathematical optimization,Search algorithm,Crossover,Evolutionary algorithm,Computer science,Sampling bias,Beam search,Robustness (computer science),Artificial intelligence,Machine learning,Genetic algorithm,Bounded function
Conference
1-55860-878-8
Citations 
PageRank 
References 
5
0.68
7
Authors
2
Name
Order
Citations
PageRank
Hiroshi Someya1253.74
Masayuki Yamamura224237.62