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Titlebook: Genetic Programming Theory and Practice VIII; Rick Riolo,Trent McConaghy,Ekaterina Vladislavleva Book 2011 Springer Science+Business Media

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樓主: Fuctionary
31#
發(fā)表于 2025-3-27 00:03:15 | 只看該作者
Covariant Tarpeian Method for Bloat Control in Genetic Programming, in such a way to guarantee that the mean program size will either keep a particular value (e.g., its initial value) or will follow a schedule chosen by the user. The mathematical derivation of the technique as well as its numerical and empirical corroboration are presented.
32#
發(fā)表于 2025-3-27 03:10:23 | 只看該作者
Composition of Music and Financial Strategies via Genetic Programming,applications, a specialized genome representation is used in order to break the problem down into smaller instances and put them back together. Results showing the applicability of the approaches are presented.
33#
發(fā)表于 2025-3-27 08:40:38 | 只看該作者
34#
發(fā)表于 2025-3-27 11:32:12 | 只看該作者
35#
發(fā)表于 2025-3-27 16:53:15 | 只看該作者
36#
發(fā)表于 2025-3-27 21:12:01 | 只看該作者
37#
發(fā)表于 2025-3-27 22:29:19 | 只看該作者
Ensemble Classifiers: AdaBoost and Orthogonal Evolution of Teams,noise - suggesting that the hierarchical approach is less subject to over-fitting than voting techniques. The results also suggest that there are specific problems and features of problems that make thembetter suited for different training algorithms and different cooperation mechanisms.
38#
發(fā)表于 2025-3-28 02:21:11 | 只看該作者
Age-Fitness Pareto Optimization,tion complexities and number of variables. Our results indicate that the multi-objective approach identifies the exact target solution more often than the age-layered population and standard population methods. The multi-objective method also performs better on higher complexity problems and higher
39#
發(fā)表于 2025-3-28 08:36:34 | 只看該作者
40#
發(fā)表于 2025-3-28 13:27:16 | 只看該作者
Genetic Programming Transforms in Linear Regression Situations, given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistica
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