Titlebook: Genetic Programming Theory and Practice II; Una-May O’Reilly,Tina Yu,Bill Worzel Book 2005 Springer-Verlag US 2005 Algorithms.Automat.algo

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Using Genetic Programming to Search for Supply Chain Reordering Policies,line their method that integrates a supply chain simulation with genetic programming. The simulation is used to score the population members for the evolutionary algorithm which is, in turn, used to search for members that might perform better on the simulation. The fitness of a population member re

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Cartesian Genetic Programming and the Post Docking Filtering Problem,ds, these technologies are less successful at ranking true hits correctly by binding free energy. This chapter presents the automated removal of false positives from virtual hit sets, by evolving a post docking filter using Cartesian Genetic Programming(CGP). We also investigate characteristics of C

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Listening to Data: Tuning a Genetic Programming System, into the data itself. This is discussed using examples from classification problems in molecular biology and the results and “rules of thumb” developed to tune the GP system are reviewed in light of current GP theory.

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Genetic Programming of an Algorithmic Chemistry,We introduce a new method of execution for GP-evolved programs consisting of register machine instructions. It is shown that this method can be considered as an artificial chemistry. It lends itself well to distributed and parallel computing schemes in which synchronization and coordination are not an issue.

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Incident Detection on Highways,This chapter discusses the development of the Low-occupancy INcident Detection Algorithm (LINDA) that detects night-time motorway incidents. LINDA is undergoing testing on live data and deployment on the M5, M6 and other motorways in the United Kingdom. It was developed by the authors using Genetic Programming.

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https://doi.org/10.1007/b101112Algorithms; Automat; algorithm; computer; genetic programming; learning; machine learning; programming; algo