Title :
A genetic algorithm for solving the CEC´2013 competition problems on real-parameter optimization
Author :
Elsayed, Saber M. ; Sarker, Ruhul A. ; Essam, Daryl L.
Author_Institution :
Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, ACT, Australia
Abstract :
Many genetic algorithms variants have been introduced for solving different classes of optimization problems. The success of any GA depends on the design of its search operators, as well as its parameters. In this paper, we propose a new three-parent crossover. In addition, we design a diversity operator which works with an archive of selected individuals. The algorithm has been applied to solve all the CEC´2013 competition problems on real-parameter optimization. The solutions obtained are either optimal or very close to the known best solutions.
Keywords :
genetic algorithms; mathematical operators; search problems; CEC´2013 competition problems; diversity operator; genetic algorithm variants; optimization problems; real-parameter optimization; search operators; three-parent crossover; Algorithm design and analysis; Evolutionary computation; Genetic algorithms; Optimization; Sociology; Statistics; Vectors; Numerical optimization; genetic algorithms; multi-parent crossover;
Conference_Titel :
Evolutionary Computation (CEC), 2013 IEEE Congress on
Conference_Location :
Cancun
Print_ISBN :
978-1-4799-0453-2
Electronic_ISBN :
978-1-4799-0452-5
DOI :
10.1109/CEC.2013.6557591
