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- W2070505841 abstract "The field of natural computation is one of the fastest growing areas in computer science and engineering. Although its origins can be traced back to the cybernetics movement of the 1950s, it has undergone a renaissance since the mid-1980s, primarily due to the fundamental work by pioneers (for example, John Holland, Ken De Jong, David Goldberg, John Koza, Lawrence Fogel) in the genetic and evolutionary algorithms domain. Problem solving techniques based on simulated evolution and learning encapsulate a wide variety of natural computation implementations. These algorithms, implemented as computer simulations, typically include a twostep iterative process: the generation of random variation followed by selection. As the model is iterated, good (optimal) solutions emerge. Evolutionary algorithms typically do not make assumptions about the underlying search space. As such, they provide a means for tackling hard practical problems—ones involving large, complex search spaces. Over the past decades evolutionary optimization and learning algorithms have been applied successfully to a wide variety of real-world problems. This Special Issue brings together recent contributions in the evolutionary optimisation and learning domains, spanning a diverse range of topics. The four papers included in this Special Issue, were among the invited submissions from SEAL08 (the 7th International Conference on Simulated Evolution and Learning). Each of the submitted papers was substantially revised and extended based on the original conference version. The extended papers were then rigorously reviewed in two rounds by at least three anonymous reviewers. The high quality contributions introduced below clearly reflect the strong interest in simulated evolution and learning as a practical problem solving vehicle." @default.
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- W2070505841 date "2009-11-18" @default.
- W2070505841 modified "2023-10-16" @default.
- W2070505841 title "Special issue on simulated evolution and learning" @default.
- W2070505841 doi "https://doi.org/10.1007/s12065-009-0033-0" @default.
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