By Witold Kosinski
With the new tendencies in the direction of significant information units and demanding computational strength, mixed with evolutionary algorithmic advances evolutionary computation is changing into even more appropriate to perform. goal of the ebook is to provide contemporary advancements, cutting edge principles and ideas in part of a massive EA box.
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Extra info for Advances in Evolutionary Algorithms
However, it soon became clear that for most practical tasks the binary encoding originally used by Holland was not at all sufficient. Accordingly many different encodings, and also necessary new crossover and mutation operators, were introduced which showed qualitatively very diverse behavior. , 2000). Since then GAs have been successfully applied to a wide range of problems including many combinatorial optimization problems, multimodal function optimization, machine learning, and the evolution of complex structures such as neural networks.
3--14, 1994. E. (1989) Genetic Alogorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, 1989. H. (1975). Adaption in Natural and Artificial Systems. University of Michigan Press, 1975. M. K. (1990). Time Series. Edward Arnold, London, 1990. R. (1992). Genetic Prohramming: On the Programming of Computers by means of Natural Selection. The MIT Press, 1992. Langdon, B. & Poli, R. (2002). Foundations of Genetic Programming. Springer-Verlag Berlin Heidelberg New York, 2002. Michaliwicz, Z.
An exemplary Multi-ROC chart. 3 Timeseries analysis There is a lot of experience using the HeuristicModeler for solving time series problems on data recorded in the context of mechatronical systems. , 2005b) we report on models trained for the NOx emissions of Diesel engines using the GP-based identification method incorporated in the HeuristicModeler. Fig. 10 and 11 show the evaluation of one of these models using the HeuristicModelAnalyzer: Apart from drawing the (original and estimated) values and a graphical representation of the formula as a structure tree, an overview of the errors distribution is given.