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Titlebook: Genetic Programming; 18th European Confer Penousal Machado,Malcolm I. Heywood,Kevin Sim Conference proceedings 2015 Springer International

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發(fā)表于 2025-3-23 10:05:08 | 只看該作者
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發(fā)表于 2025-3-23 15:03:52 | 只看該作者
Cartesian GP in Optimization of Combinational Circuits with Hundreds of Inputs and Thousands of Gatehe goal of the evolutionary circuit optimization is to minimize the number of gates (or other non-functional parameters) of already functional circuit. The method combines a circuit simulation with a formal verification in order to detect the functional inequivalence of the parent and its offspring.
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發(fā)表于 2025-3-23 21:31:14 | 只看該作者
Genetic Programming for Feature Selection and Question-Answer Ranking in IBM Watsonon uses machine learning over a large heterogeneous feature set derived from many distinct natural language processing algorithms to identify correct answers. This paper develops a Genetic Programming (GP) approach for feature selection in Watson by evolving ranking functions to order candidate answ
14#
發(fā)表于 2025-3-24 01:08:36 | 只看該作者
Automatic Evolution of Parallel Recursive Programsen require the application of skilled parallelization knowledge to fully realize the potential of the hardware. This paper automates the process by using Grammatical Evolution (GE) to exploit the multi-cores through the evolution of . parallel programs. We present Multi-core Grammatical Evolution (M
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發(fā)表于 2025-3-24 05:17:10 | 只看該作者
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發(fā)表于 2025-3-24 07:42:03 | 只看該作者
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發(fā)表于 2025-3-24 12:02:07 | 只看該作者
T, – A Framework for Template-Method Hyper-Heuristicst discuss the need for such an approach, presenting Quicksort as an example. We provide a functional definition of template-method hyper-heuristics, describe how this is implemented by ., and show how . may be invoked using simple client-code. Finally, we describe experiments using . to define a ‘hy
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發(fā)表于 2025-3-24 18:26:30 | 只看該作者
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