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Genetic Algorithms and Neural Networks

401318 - Genetic Algorithms and Neural Networks
Faculty

Faculty of Information Technology

Department

Computer Science

Credit Hours

3

Pre-requisite

401314

Co-requisite

-

Distribution

3+0
This course gives an introduction to AI search methods, neural networks (NNs), single-layer perceptions, ADALINE, perception learning, and multi-layer feed forward neural networks. Also discussed are supervised learning and back propagation, unsupervised and competitive learning? The course discusses Kohonen's self-organizing maps (SOM) and radial basis function network. An introduction to genetic algorithms (GAs) is presented including representation GA terminology and operators (crossover, mutation, inversion). Also given are the theory of GA, schema properties, implicit parallelism, selection, replacement and reproduction strategies ('roulette wheel', elitism, ring and tournament based selection), premature convergence, coding and scaling, GA advantages, disadvantages and applications and GA for evolving neural networks.