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首页> 外文期刊>American journal of applied sciences >Performance Analysis of Multi Clustered Parallel Genetic Algorithm with Gray Value | Science Publications
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Performance Analysis of Multi Clustered Parallel Genetic Algorithm with Gray Value | Science Publications

机译：灰值的多聚类并行遗传算法性能分析科学出版物

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> 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.
机译： > 问题陈述：编码是遗传算法的主要因素之一，对于复杂的问题，确定问题的最优性。编码机制是形成染色体以形成整个群体中最合适的个体的第一步。所提出的方法试图通过二进制编码和格雷编码来编码初始种群。 方法：我们为实验确定的问题是背包号p。随机产生背包问题的染色体，并将个体聚类成簇。在每个簇中，选择了亲本，并将选定的染色体用灰色编码，以供进一步的遗传算子使用。 结果：通过实现灰色编码机制，实验结果表明，使用大量人口时利润有所提高。通过改变种群大小，改变种群大小和改变突变率来实施该算法，分析了该算法的结果。 结论：拟议的遗传算法通过减少遗传算子中的一个步骤来达到最优解，从而减少了算法的执行时间。通过将突变再现算子应用于灰度值，可以在一个简单的过程中生成最适合的个体。

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