Performance Analysis of Multi Clustered Parallel Genetic Algorithm with Gray Value

Abstract

Problem statement: Encoding is one of the major factors in genetic algorithms, for a complex problem the optimality for the problem is determined. The encoding mechanism is the initial step which forms the chromosome to form the best fit individuals in the entire population. The proposed method tries to encode the initial population by the binary encoding and by gray encoding. Approach: The problem we had identified for the experiment was a Knapsack p. The chromosomes were generated for the knapsack problem with random and the individuals were clustered into in clusters. In each cluster the parent was selected and the selected chromosome was gray coded for further genetic operators. Results: By implementing gray encoding mechanism, the experiment result shows the improvement in profit when large population was used. The results were analyzed for the algorithm by implementing it by changes in population size, changes in group size and change in mutation rate. Conclusion: The Proposed genetic algorithm reduces the execution time of the algorithm by reducing one step in the genetic operators to reach the optimal solution. The best fit individual is produced in a simple process by applying a mutation reproduction operator to the gray value.