Paper Info
Title
Multi-Strategy Genetic Algorithm for Multimodal Optimization.
Abstract
Multimodal optimization (MMO) is the problem of finding many or all global and local optima. In recent years many efficient nature-inspired techniques (based on ES, PSO, DE and others) have been proposed for real-valued problems. Many real-world problems contain variables of many different types, including integer, rank, binary and others. In this case, the weakest representation (namely binary representation) is used. Unfortunately, there is a lack of efficient approaches for problems with binary representation. Existing techniques are usually based on general ideas of niching. Moreover, there exists the problem of choosing a suitable algorithm and fine tuning it for a certain problem. In this study, a novel approach based on a metaheuristic for designing multi-strategy genetic algorithm is proposed. The approach controls the interactions of many search techniques (different genetic algorithms for MMO) and leads to the self-configuring solving of problems with a priori unknown structure. The results of numerical experiments for classical benchmark problems and benchmark problems from the CEC competition on MMO are presented. The proposed approach has demonstrated efficiency better than standard niching techniques and comparable to advanced algorithms. The main feature of the approach is that it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.
Year
DOI
Venue
2015
10.5220/0005592000550063
IJCCI (ECTA)
Keywords
Field
DocType
Multimodal Optimization,Self-Configuration,Genetic Algorithm,Metaheuristic,Niching
Mathematical optimization,Algorithm design,Local optimum,Computer science,Meta-optimization,A priori and a posteriori,Cluster analysis,Quality control and genetic algorithms,Genetic algorithm,Metaheuristic
Conference
Volume
ISBN
Citations 
1
978-1-5090-1968-7
0
PageRank 
References 
Authors
0.34
13
1
Name
Order
Citations
PageRank
Evgenii Sopov100.34