Title :
Improved genetic algorithm to enhance the ability of local search
Author_Institution :
Wuhan Univ. of Sci. & Technol. City Coll., Wuhan, China
Abstract :
We proposed an improved genetic algorithm (GA) based on floating-point encoding, and greatly enhanced its local adjustment capability using linear crossover operator, dynamic mutation operator, and the substitution and retention strategies used at different population evolutionary stages. Numerical simulation further validated the effectiveness of the improved GA.
Keywords :
genetic algorithms; search problems; GA; dynamic mutation operator; floating-point encoding; improved genetic algorithm; linear crossover operator; local search; numerical simulation; population evolutionary stages; retention strategy; substitution strategy; Biological cells; Encoding; Genetic algorithms; Genetics; Optimization; Sociology; Statistics; Approximate optimal solution; Binary encoding; Floatingpoint encoding; Genetic algorithm; Global optimal solution;
Conference_Titel :
Mechatronics and Control (ICMC), 2014 International Conference on
Print_ISBN :
978-1-4799-2537-7
DOI :
10.1109/ICMC.2014.7231936
