Adaptive and Natural Computing Algorithms: 8th International by Marek Krȩtowski, Marek Grześ (auth.), Bartlomiej

By Marek Krȩtowski, Marek Grześ (auth.), Bartlomiej Beliczynski, Andrzej Dzielinski, Marcin Iwanowski, Bernardete Ribeiro (eds.)

The quantity set LNCS 4431 and LNCS 4432 constitutes the refereed lawsuits of the eighth foreign convention on Adaptive and traditional Computing Algorithms, ICANNGA 2007, held in Warsaw, Poland, in April 2007.

The 178 revised complete papers awarded have been conscientiously reviewed and chosen from a complete of 474 submissions. The ninety four papers of the 1st quantity are geared up in topical sections on evolutionary computation, genetic algorithms, particle swarm optimization, studying, optimization and video games, fuzzy and tough structures, simply as class and clustering. the second one quantity comprises eighty four contributions with regards to neural networks, help vector machines, biomedical sign and snapshot processing, biometrics, laptop imaginative and prescient, in addition to to regulate and robotics.

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Extra resources for Adaptive and Natural Computing Algorithms: 8th International Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceedings, Part I

Sample text

M. J. B.

From Fig. 5 %. 6 Conclusions This paper proposes a promising approach based on the use of Evolutionary Algorithms, Interval Arithmetic and procedures to estimate the yield design as an alternative technique to obtain optimal centring design. The approach is direct and intuitive. The excellent results obtained suggest that the proposed approach has great potential in dealing with difficult system design problems. The use of a guaranteed method based on Interval Arithmetic such as SIVIA needs more time than deterministic or statistical approaches.

The method looks promising, but since several relevant implementation details are missing in [7] it is not possible to exactly follow the idea. In particular the question about how to build the initial population in the pre-alignment space (which is crucial for the quality of obtained result) is not addressed. Another question that can be raised concerns the usefulness of genetic operators proposed in [7]. Our claim is that mutation operator of the form presented in [7] is inefficient. Additionally some refinements are proposed to the definition of a crossover operator.

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