Optimization of Performance of Genetic Algorithm for 0-1 Knapsack Problems Using Taguchi Method
In this paper, a genetic algorithm (GA) is developed for solving 0-1 knapsack problems (KPs) and performance of the GA is optimized using Taguchi method (TM). In addition to population size, crossover rate, and mutation rate, three types of crossover operators and three types of reproduction operators are taken into account for solving different 0-1 KPs, each has differently configured in terms of size of the problem and the correlation among weights and profits of items. Three sizes and three types of instances are generated for 0-1 KPs and optimal values of the genetic operators for different types of instances are investigated by using TM. We discussed not only how to determine the significantly effective parameters for GA developed for 0-1 KPs using TM, but also trace how the optimum values of the parameters vary regarding to the structure of the problem.
KeywordsGenetic Algorithm Design Factor Knapsack Problem Crossover Operator Crossover Rate
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- 7.Gen, M., Cheng, R.: Genetic Algorithms and Engineering Design. John Wiley&Sons, New York (1997)Google Scholar
- 9.Taguchi, G.: Systems of Experimental Design. Unipub Kraus International Publishers, New York (1987)Google Scholar
- 11.Antony, J., Roy, R.K.: Improving the Process Quality Using Statistical Design of Experiments: A Case Study. Quality Assurance 6, 87–95 (1999)Google Scholar
- 12.Fowlkes, W.Y., Creveling, C.M.: Engineering Methods for Robust Product Design. Addison-Wesley, Reading (1995)Google Scholar
- 13.Ross, P.J.: Taguchi Techniques for Quality Engineering, 2nd edn. McGraw-Hill, New York (1996)Google Scholar
- 17.Roy, R.K.: A Primer on the Taguchi Method. VNR Publishers, New York (1999)Google Scholar
- 18.Ozalp, A., Anagun, A.S.: Analyzing Performance of Artificial Neural Networks by Taguchi Method: Forecasting Stock Market Prices. Journal of Statistical Research 2, 29–45 (2003)Google Scholar