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Imagine for convenience that the values of the observations are recorded on a hundred cards; these cards must be separated into two heaps, the lefthand heap containing the twenty lowest cards, and the right-hand heap containing the rest We can now regard the problem as one of designing, as it were, the rules for a game of patience, whose outcome is the one we desire, and which has the delightful property that it always comes out Perhaps the first idea which occurs to us is to sort the cards into ascending order; for then the selection of the required twenty cards is trivial All that is needed is to deal the first twenty cards off the top of the pack So all we have to do is to find some efficient method of sorting the pack.

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Gathering all of the terms together, obtain:

But further consideration shows that it would be a waste of time to sort the whole pack, when all we require is to single out twenty of them, the twenty cards with smallest value So we turn attention again to this problem Our second proposed method is to look through the whole pack for the smallest card, and remove it; then look for the smallest again in the reduced pack; and to repeat the process until twenty cards have been singled out Before accepting this solution, we ask ourselves how efficient the method is In order to find the lowest card in a pack it is in principle necessary to look at every card in the pack, ie a hundred cards; to find the second lowest card requires ninety-nine inspections, and so on.

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Figure 5.10 Difference in total energy for the two- and three-dimensional cases, as a function of donor position rd across the 60 A CdTe well

1989] D Whitley, T Starkweather, D Fuquay, Scheduling Problems and the Travelings Salesmen: The Genetic Edge Recombination Operator, in JD Schaffer (ed), Proceedings of the 3rd International Conference on Genetic Algorithms, Morgan Kaufmann, 1989, pp 116 121 [Whitley and Bogart 1990] D Whitley, C Bogart, The Evolution of Connectivity: Pruning Neural Networks using Genetic Algorithms, Proceedings of the IEEE International Joint Conference on Neural Networks, Vol 1 1990, pp 134 137..

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Thus assuming optimistically that each inspection takes a second, the total time taken will be about half an hour a rather daunting prospect Going back to the original computer program of 100 thousand observations, and assuming our computer can examine about a hundred thousand observations in one second, it would take about five hours to select the least 20 000 observations So it is worth while to seek an alternative more efficient solution As our third idea, it may occur to us that if we happened to know the value of the observation which was the twenty-first smallest one, we could use this as a sort of borderline value in the process of splitting the pack into two heaps For example, supposing we.

Figure 5.11 The electron wave function u, as in equation 5.7, for the donor positions given on the right hand axis

So, in each case, the distributMty property with respect to a binary operator extends to a distributMty property with respect to any finite number of operands, including zero. (The case when there is only one operand is trivial.) Formally, the general distributMty for quantifications is as follows. Suppose both and are associative and symmetric operators, with units 1 and 1 , respectively. Suppose / is a function with the properties that

Using this form in equation (5.78) then:

[White and Ligomenides 1993] D White, P Ligomenides, GANNet: A Genetic Algorithm for Optimizing Topology and Weights in Neural Network Design, International Workshop on Artificial Neural Networks, J Mira, J Cabestany, A Prieto (eds), New Trends in Neural Computation, in the series Lecture Notes in Computer Science, Springer-Verlag, Berlin, 1993, pp 332 327 [Widrow 1987] B Widrow, ADALINE and MADALINE - 1963, Plenary Speech, Proceedings of the First IEEE International Joint Conference on Neural Networks, Vol 1, San Diego, 1987, pp 148 158 [Widrow and Lehr 1990] B Widrow, MA Lehr, 30 Years of Neural Networks: Perceptron, Madaline and Backpropagation, Proceedings of the IEEE, Vol 78, 1990, pp 1415 1442 [Williams 1995] PM Williams, Bayesian Regularization and Pruning Using a Laplace Prior, Neural Computation, Vol 7, 1995, pp 117 143.

/. 1 = 1

which, using equation (5.64) gives the following:

and, for all x and y,

Again, substituting r' for r , then gives:

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