Performance analysis of various Tuning methods for Genetic algorithm

Abstract

Genetic Algorithms are a robust search technique that mimics the newlineprocess of natural evolution to direct the search procedure from a randomized newlineinitialization to a more prospective direction in a very large search space newlineIn recent years GA is widely used in multiple scientific domains newlinethe need for a high performing genetic algorithm is essential The newlineperformance of the genetic algorithm is measured by the speed of the search newlineor by the reliability of the algorithm Since from the beginning of GA newlinesignificant process has been made in various aspects of GA in attempt to newlineimprove the performance further on all types of problems and search space newlineThe performance of the genetic algorithm is very much attributed to newlinethe premature convergence of the individuals in the population and diversity newlinein turn is determined by the chromosome structure population size and newlineselection pressure newlineThis research tries to improve the performance of the genetic newlinealgorithm by tuning various attributes of genetic algorithm The tuning is newlinedone by changing the chromosome structure at the initial search space and newlineby reducing the initial population size All the tuning methods are applied in newline0 1 knapsack problem to analyze the performance newlineFirst tuning method is done by modifying the structure of the newlineChromosome Generally in GA the chromosomes are represented in the form newlineof binary coded strings Gray coding is one of the coding scheme Here gray newlinecoding is applied to convert binary represented strings to gray strings Gray newlinecoding is another way of coding parameters into bits which has the property newlinethat an increase of one step in the parameter value corresponds to a change of newlinea single bit in the code newline newline