Advances in Evolutionary Algorithms by Witold Kosinski

By Witold Kosinski

With the hot tendencies in the direction of immense facts units and demanding computational energy, mixed with evolutionary algorithmic advances evolutionary computation is turning into even more suitable to perform. objective of the e-book is to provide fresh advancements, cutting edge principles and ideas in part of a massive EA box.

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We have exemplarily shown how regression, classification and time series problems can be attacked using this algorithm. Especially in the context of analyzing time series problems of mechatronical systems as well as medical data sets we have already achieved very good results. Furthermore, we have also demonstrated how to analyze the results for data mining problems as well as selected aspects of the underlying enhanced evolutionary algorithm. 9. Acknowledgements The work described in this paper was partially done within the Translational Research Program project L284-N04 "GP-Based Techniques for the Design of Virtual Sensors" sponsored by the Austrian Science Fund (FWF).

A pointer to one of the objects' properties) or a constant. The nonterminal nodes have to be selected from a library of possible functions, a pool of potential nonlinear model structural components; as with every GP modeling process, the selection of the library functions is an important part since this library should be able to represent a wide range of systems. When the evolutionary algorithm is executed, each individual of the population represents one structure tree. Since the tree structures have to be usable by the evolutionary algorithm, mutation and crossover operators for the tree structures have to be designed.

Then a reunification from n to (n-1) subpopulations is performed by joining an appropriate number of adjacent subpopulation members. Metaphorically speaking this means, that the villages (subpopulations) at the beginning of the evolutionary process are slowly growing together to bigger towns, ending up with one big city containing the whole population at the end of evolution. By this approach of widthsearch essential building blocks can evolve independently in different regions of the search space at the beginning and during the evolutionary process.

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