A. Ratle. Optimal sampling strategies for learning a tness model. In Proceedings of 1999 Congress on Evolutionary Computation, volume 3, pages 2078{ 2085, Washington D.C., July 1999.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....than do them randomly. However, the reduction of tness evaluations may not be signi cant. Use of approximate tness models through tness evaluations. In most research, the approximate model has been directly used in tness evaluations in order to reduce the number of tness calculation [44, 56, 57, 48, 64, 65, 20, 21, 51, 12, 59, 23, 36, 37, 38, 27, 22]. Di erent approximate models, including polynomials, kriging models and neural networks have been applied. An interesting idea in [22] is that a con dence interval for the tness estimation is calculated to modify the model prediction so that the search in unexplored regions is encouraged. It ....

....as in [5, 66, 40] Fixed Evolution Control. The importance to use both the approximate model and the original function for tness evaluation has been recognized [64] There are generally two approaches to evolution control, one is individual based [30, 12, 36] and the other is generation based [64, 65, 36]. By individual based control, it is meant that in each generation, some of the individuals use the approximate model for tness evaluation and others the original function for tness evaluation. In individual based evolution control, either a random strategy or a best strategy can be used to ....

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A. Ratle. Optimal sampling strategies for learning a tness model. In Proceedings of 1999 Congress on Evolutionary Computation, volume 3, pages 2078{ 2085, Washington D.C., July 1999.

.... algorithms have become increasingly robust and easy to use as a global optimization method [4, 2, 12, 25] Using approximation and or local search techniques in evolutionary algorithms have provided many successful examples, for example the evolutionary algorithms with kriging approximation [15, 16], the genetic algorithms with local search [23, 9] the crossover operators with approximation concepts [19, 1] and the evolutionary algorithm with both landscape approximation and local search (LALS) 11] The LALS algorithm [11] demonstrated high reliability in nding the global optimum of the ....

A. Ratle. Optimal sampling strategies for learning a tness model. In Proceedings of the 1999 Congress on Evolutionary Computation, volume 3, pages 2078-2085, Piscataway, NJ, 1999. IEEE Press.

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