Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
A Comparative Study of Steady State and Generational Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Neural Networks, Fuzzy Logic and Genetic Algorithms
Neural Networks, Fuzzy Logic and Genetic Algorithms
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 05
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
CIXL2: a crossover operator for evolutionary algorithms based on population features
Journal of Artificial Intelligence Research
Neural-network-based adaptive hybrid-reflectance model for 3-D surface reconstruction
IEEE Transactions on Neural Networks
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Optimisation is the most interesting problems to be tested by using Artificial Intelligence (AI) methods because different optimal results will be obtained 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 techniques implemented. The techniques for each operation have shown different performance in obtaining the time, minimum and average values for benchmark functions.