A Hybridized Chemotactic Genetic Algorithm for Optimization

摘要

This paper presents a new hybridized genetic algorithm for optimization of a function with several variables. To construct the proposed algorithm, a new operator by the name of chemotactic operator has been constructed using the chemotaxis behaviour of the E. coli bacteria. Then this integrated genetic algorithm has been used to obtain minimum optimal of a single objective function. Finally, some numeric simulations have been carried out to show the effectiveness of the proposed algorithm comparing the results of simple genetic algorithm using some benchmark functions.