Download Artificial Neural Nets and Genetic Algorithms: Proceedings by Dr. George D. Smith, Dr. Nigel C. Steele, Dr. Rudolf F. PDF

By Dr. George D. Smith, Dr. Nigel C. Steele, Dr. Rudolf F. Albrecht (auth.)

This is the 3rd in a sequence of meetings committed basically to the speculation and functions of synthetic neural networks and genetic algorithms. the 1st such occasion was once held in Innsbruck, Austria, in April 1993, the second one in Ales, France, in April 1995. we're happy to host the 1997 occasion within the mediaeval urban of Norwich, England, and to carryon the positive culture set by way of its predecessors of delivering a peaceful and stimulating atmosphere for either tested and rising researchers operating in those and different, similar fields. This sequence of meetings is exclusive in recognising the relation among the 2 major issues of man-made neural networks and genetic algorithms, every one having its foundation in a common strategy basic to lifestyles on the earth, and every now good validated as a paradigm basic to carrying on with technological improvement during the resolution of advanced, commercial, advertisement and monetary difficulties. this can be good illustrated during this quantity through the varied functions of either paradigms to new and tough difficulties. The 3rd key subject matter of the sequence, for this reason, is the combination of either applied sciences, both by using the genetic set of rules to build the simplest community structure for the matter in hand, or, extra lately, using neural networks as approximate health capabilities for a genetic set of rules trying to find strong options in an 'incomplete' resolution house, i.e. one for which the health isn't really simply demonstrated for each attainable answer instance.

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Extra info for Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Norwich, U.K., 1997

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Machine Learning, (22), 1996. [11] S. Nolfi and D. Parisi. Evolving non-trivial behaviours on real robots: An autonomous robot that picks up objects. In M. Gori and G. Soda, editors, Proceedings of the fourth congress of the Italian Association of Artificial Intelligence, pages 243-254, 15 Viale Marx, 00137 - Rome - Italy, 1995. Springer-Verlag. [12] M. A. Potter, K. A. De Jong, and J. J. Grefenstette. A coevolutionary approach to learning in sequential decision rules. In Proc. 6th Int. Con/. on Genetic Algorithms, Pitsburgh, July 1995.

4 higher accuracy of nearest neighbour while largely preserving the substantially reduced computational cost associated with neural networks. Branch of Neural Networks Different Network Types The individual networks can be of various types. IT an ensemble with the fewest networks is sought, any given training set is learned by several different network types, and the network that has learned the most points included in the ensemble. The disadvantage of this is that it is more time consuming. However, testing times can be dramatically reduced.

Breeding then occurs by selecting single parents using rank-based selection and applying point mutation or random sub-tree replacement to generate new agents. The top 20% of the population is retained each generation. In theory, it is possible to co-evolve several populations at the same time, but in practice interference between non-converged populations seems to make this difficult. Therefore we currently only evolve one new population each time we try to learn some new sub-skill or increase in task complexity, and freeze the others.

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