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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https://doi.org/10.1007/978-1-4939-0375-7Artificial evolution; Evolution of models; Feature selection; Genetic programming; Genetic programming a

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978-1-4939-5563-3Springer Science+Business Media New York 2014

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Optimizing a Cloud Contract Portfolio Using Genetic Programming-Based Load Models, quoted pay-by-the-hour, fixed price (“on demand”) model against the lower-cost-per-hour rate of a “reserved contract”. These tariff structures offer a significantly reduced cost per server hour (up to 50 %), in exchange for an up-front payment by the consumer. In order to reduce costs using these r

Simulate

Maintenance of a Long Running Distributed Genetic Programming System for Solving Problems Requiringegies has executed for extended durations (months to years) on nodes distributed around the globe. ECStar system instances are almost never stopped and restarted, though they are resource elastic. Instead they are interactively redirected to different parts of the problem space and updated with up-t

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Grounded Simulation: Using Simulated Evolution to Guide Embodied Evolution,(compared to magnetic disks), requires low power and generates very little heat. Unfortunately, it has a relatively short life span, as it wears out over time, compromising its ability to retain data. Previous work has used GAs to tune the control parameters of Flash to trade . for . and, although s

Incisor

Applying Genetic Programming in Business Forecasting,ing. Of special interest are the forecasting methods based on explanatory variables (economic drivers), the most popular of which is the Auto-Regressive Integrated Moving-Average with eXplanatory variables (ARIMAX) model. A limitation of this approach, however, is the assumption of linear relationsh