Hybrid genetic algorithms for machine learning

Describes the basic genetic algorithm and then discuss some ways in which it may be hybridized with other types of optimisation techniques. The comments on hybridization are of two kinds. First, three general principles for hybridizing genetic algorithms and other algorithms are given. These principles have often generated hybrid optimization systems that perform better than the systems they arose from. Second, two examples of such hybrid systems, one of optimizing the design of packet-switching telecommunications networks, and one of training neural networks, are described.<
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