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.