Abstract

The goal of this study of threshold acceptance algorithm TA simulated annealing algorithm SA and genetic algorithm GA is to determine strength of Genetic Algorithm over other algorithm It gives a clear idea of how genetic algorithm works It gives the idea of various sub methods used in genetic algorithm to improve the results and outcome Basically genetic algorithm and all traditional heuristic methods are used for optimization Optimization problems are class NP complete problems Genetic algorithm can be viewed as an optimization technique which exploits random search within a defined search space to solve a problem by some intelligence ideas of nature In this work we have done Comparative analysis of Threshold Acceptance Algorithm Simulated Annealing Algorithm and Genetic Algorithm by considering different test functions and its constraints to minimize the test functions