Titlebook: Genetic Programming Theory and Practice V; Rick Riolo,Terence Soule,Bill Worzel Book 2008 Springer-Verlag US 2008 Algorithms.Multi-agent s

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Tuning of Disk Resident Data Structuresof this approach is that all available data may be used in the model development rather than a partition into training, test and validation subsets. The result is constituent models are more accurate without risk of over-fitting, the ensemble predictions are more accurate and the ensemble prediction

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Infelicity

https://doi.org/10.1007/978-981-19-3032-4hm — take widely different approaches to combine these different portfolio metrics. The results show that the multi-objective algorithms do produce well-balanced portfolio performance, with the constrained fitness function performing much better than the sequential and parallel multi-objective algor

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Using Stored Procedures, Views, and Triggersmployed do not always have the semantics needed to make sense of the distinction between occupied and unoccupied terraces. In this paper we focus on the latter reason. Here, GP and CA are used to add semantics to rules to make them more understandable to experts in the area. Future work will examine

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Genetic Programming: Theory and Practice,a rigorous, detailed theory that is mature enough to guide the development of GP systems to solve specific problems contributes to this weakness. Thus, there remains an on-going need to both strengthen theory and to keep it closely tied to the practice of GP. The Genetic Programming Theory and Pract

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Better Solutions Faster: Soft Evolution of Robust Regression Models InParetogeneticprogramming, we observed that those evolutions that started from the smallest subset sizes (10%) consistently led to results that are superior in terms of the goodness of fit, consistency between independent runs, and computational effort. Our experience indicates that solutions obtained using this approach are

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Towards an Information Theoretic Framework for Genetic Programming,uld be stronger than that of error, improving evolvability. We support these arguments with simulations using a logic function benchmark and a time series application. For a chaotic time series prediction problem, for instance, the proposed approach avoids familiar difficulties (premature convergenc

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Investigating Problem Hardness of Real Life Applications,apies, maximizing medical success rate and minimizing toxic effects. The experimental results presented in this chapter show that the Negative Slope Coefficient seems to be a reasonable tool to characterize the difficulty of these problems, and can be used to choose the most effective Genetic Progra

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