A. Selamat et al. (Eds.): ACIIDS 2013, Part I, LNAI 7802, pp. 255–264, 2013. © Springer-Verlag Berlin Heidelberg 2013 Performance of Different Techniques Applied in Genetic Algorithm towards Benchmark Functions Seng Poh Lim and Habibollah Haron Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia lawrencess87@yahoo.com, habib@utm.my Abstract. Optimisation is the most interesting problems to be tested by using Artificial Intelligence (AI) methods because different optimal results will be ob- tained when different methods are implemented. Yet, there is no exact solution from the methods implemented because random function is usually applied. Genetic algorithm is a popular method which is used to solve the optimisation problems. However, no any methods can execute perfectly because the way of the method performs is different. Therefore, this paper proposed to compare the performance of GA with different operation techniques by using the benchmark functions. This can prove that different techniques applied in the operations can let GA produces different result. Based on the experiment result, GA is proved to perform well in the optimisation problems but it highly depends on the tech- niques implemented. The techniques for each operation have shown different performance in obtaining the time, minimum and average values for benchmark functions. Keywords: Genetic Algorithm, Optimisation, Benchmark Functions, Performance. 1 Introduction Optimisation is the most interesting problems to be tested by using Artificial Intelli- gence (AI) or Soft Computing methods. This is because different optimal results will be obtained when different methods are implemented. However, there is no exact solution from the methods implemented because random function is usually applied in AI method. Basically, same optimisation problems were tested by using different methods because the researchers want to test the performance of the method in obtain- ing the result. If the result obtained is the most minimum and approximate to the ac- tual optimum solution, hence this method is considered contains the best performance compared to the others. Genetic algorithm (GA) is a popular soft computing technique which is used to solve the optimisation problems. Besides that, it can also be used to deal with schedul- ing, surface fitting and searching problems. GA has been widely used to deal with different kind of optimisation problems and it able to find and produce a good