Abstract
In this chapter, the basic definition of Genetic Algorithm (GA) and some of the main operations applied in GA are explained. In addition, Swarm Intelligence (SI) is briefly explained as the new branch of intelligent behavior of nature phenomena. Although PSO has been explained in past chapters, this chapter explains PSO in detail and an example of the way PSO works is provided for better understanding. Some of the differences of Particle Swarm Optimization (PSO) and GA are provided and readers will learn how to use GA and PSO for training the neural network. The experiments and contents in this chapter are from the study by Nuzly (2006) in her thesis entitled “Particle Swarm Optimization for Neural Network Learning Enhancement”.
Reference39 articles.
1. Al-kazemi, B., & Mohan, C. K. (2002). Training feedforward neural network using multi-phase particle swarm optimization. In Proceedings of the 9th International Conference on Neural Information Processing, New York.
2. Bergh, V. D. F. (1999). Particle swarm weight initialization in multi-layer perceptron artificial neural networks. Paper accepted for ICAI. Durban, South Africa.
3. Cooperative learning in neural networks using particle swarm optimizers.;V. D. F.Bergh;South African Computer Journal,2000
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