Biogeography-Based Optimizer (BBO) for training Multi-Layer Perceptron (MLP)

Biogeography-Based Optimizer (BBO) is employed as a trainer for Multi-Layer Perceptron (MLP)

Biogeography-Based Optimizer (BBO) is employed as a trainer for Multi-Layer Perceptron (MLP). The current source codes are the demonstration of the BBO-MLP trainer for solving the Iris classification problem. There are also other trainers in this submission: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Evolutionary Strategy (ES), and Probability-Based Incremental Learning (PBIL). The classification accuracy of BBO-MLP is calculated at the end of main.m file and compared to those of PSO, ACO, ES, GA, and PBIL. The convergence curve and classification accuracy of each algorithm are drawn at the end.
This is the demonstration source codes of the paper: S. Mirjalili, S. M. Mirjalili, A. Lewis, Let A Biogeography-Based Optimizer Train Your Multi-Layer Perceptron, Information Sciences, In press, 2014, DOI: http://dx.doi.org/10.1016/j.ins.2014.01.038

More information can be found in my personal web page: http://www.alimirjalili.com

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