Titlebook: Genetic Programming Theory and Practice XI; Rick Riolo,Jason H. Moore,Mark Kotanchek Book 2014 Springer Science+Business Media New York 20

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BLUSH

,Exploring , in a Computational Evolution System for the Genome-Wide Genetic Analysis of Alzheimer’sth simulated and real data. The goal of the present study was to introduce a measure of . into the modeling process. Here, we define interestingness as a measure of non-additive gene-gene interactions. That is, we are more interested in those CES models that include attributes that exhibit synergist

electrolyte

Optimizing a Cloud Contract Portfolio Using Genetic Programming-Based Load Models,orld data. The predicted future load is subsequently used by a resource manager to optimize the amount of IaaS servers a consumer should allocate at a cloud provider, and the optimal tariff plans (from a cost perspective) for that allocation. Our results illustrate the benefits of load forecasting f

pulmonary

Maintenance of a Long Running Distributed Genetic Programming System for Solving Problems Requiring, deployment, operation and update of an instance of such a large, distributed and long running system. Moreover, we outline how ECStar is designed to allow manual guidance and re-alignment of its evolutionary search trajectory.

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PON

Explaining Unemployment Rates with Symbolic Regression,mple R.”. We conclude that the two packages tested, Eureqa and ARC, can produce models that go beyond the power of traditional stepwise regression. ARC, in particular, is able to replicate the format of published economic research because ARC contains a high level Regression Query Language (RQL). Th

PON

Uniform Linear Transformation with Repair and Alternation in Genetic Programming,and structures. ULTRA treats hierarchical programs as linear sequences and includes a repair step to ensure that syntax constraints are satisfied after variation. We show that on the drug bioavailability and Pagie-1 benchmark problems ULTRA produces significant improvements both in problem-solving p

Formidable

Evaluation of Parameter Contribution to Neural Network Size and Fitness in ATHENA for Genetic Analys of multiple GENN parameters and data noise levels on model detection and network structure. We concluded that the models produced by GENN were greatly affected by algorithm parameters and data noise levels. We also produced complex, multi-layer networks that were not produced in the previous study

HAIL

Book 2014arantee convergence to solutions in the function discovery mode; (2) Issues on model validation; (3)The need for model analysis workflows for insight generation based on generated GP solutions – model exploration, visualization, variable selection, dimensionality analysis; (4) Issues in combining di

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