Genetic Algorithms in Optimisation, Simulation and Modelling


Stender, J.,
Hillebrand, E.,
Kingdon, J.

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(reprinted 2005)


One common criticism of Artificial Intelligence (AI) is the brittleness of the solutions it produces. The suggestion is that AI systems have not scaled well beyond the relatively limited domains to which they have been applied. In the early nineties, there was a marked trend in the AI community towards real-world applications. Techniques, inspired by AI’s wider ambition to produce more intelligent machines, were not only gaining acceptance in other fields of scientific research, but also in areas such as business, commerce and industry. Moreover, there was a tendency for the techniques themselves to be developed, tested and refined within such applications. The theme seems to be, if a technique represents a genuine advance in software engineering, by definition it has commercial advantage. Nowhere is this trend more evident than in the application of genetic algorithms (Gas). What has marked out as Gas as compared to other techniques is the surprising speed with which commercial organisations have shown an interest. One of the reasons for this is that Gas seem to offer an extremely effective, general-purpose means for dealing with both complexity and scale. This book emphasizes the diversity of the GA approach by presenting detailed descriptions of Gas used for real-world optimization and for complex modelling problems.

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