DOI: 10.5176_2010-2283_1.2.33 A Precise Evolutionary Approach to Solve Multivariable Functional Optimization

Abstract — Genetic Algorithm (GA) is a stochastic search and optimization method imitating the metaphor of natural biological evolution. GA manages population of solutions instead of a single solution to find an optimal solution to a given problem. Although GA draws attention for functional optimization, it may search same point again due to its probabilistic operations that hinder its performance. In this study, we make a novel approach of standard Genetic Algorithm (sGA) to achieve better performance. The modification of sGA is investigated in selection and recombination stages and proposed Precise Genetic Algorithm (PGA). PGA searches the target space efficiently and it shows several potential advantages over the conventional GA when tested for solving functions having multiple independent variables