Download Genetic Programming Theory and Practice VIII by Michael Orlov, Moshe Sipper (auth.), Rick Riolo, Trent PDF

By Michael Orlov, Moshe Sipper (auth.), Rick Riolo, Trent McConaghy, Ekaterina Vladislavleva (eds.)

The contributions during this quantity are written by way of the most important foreign researchers and practitioners within the GP area. They study the similarities and ameliorations among theoretical and empirical effects on real-world difficulties. The textual content explores the synergy among concept and perform, generating a finished view of the cutting-edge in GP application.

Topics contain: FINCH: A approach for Evolving Java, useful Autoconstructive Evolution, The Rubik dice and GP Temporal series studying, Ensemble classifiers: AdaBoost and Orthogonal Evolution of groups, Self-modifying Cartesian GP, summary Expression Grammar Symbolic Regression, Age-Fitness Pareto Optimization, Scalable Symbolic Regression through non-stop Evolution, Symbolic Density versions, GP Transforms in Linear Regression events, Protein Interactions in a Computational Evolution approach, Composition of track and monetary options through GP, and Evolutionary paintings utilizing Summed Multi-Objective Ranks.

Readers will detect large-scale, real-world purposes of GP to various challenge domain names through in-depth shows of the newest and most important ends up in GP .

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Additional resources for Genetic Programming Theory and Practice VIII

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And McPhee, Nicholas Freitag (2008). A field guide to genetic programming. uk. (With contributions by J. R. Koza). Spector, Lee and Robinson, Alan (2002). Genetic programming and autoconstructive evolution with the Push programming language. Genetic Programming and Evolvable Machines, 3(1):7–40. Turing, Alan Mathison (1950). Computing machinery and intelligence. Mind, 59(236):433–460. Wong, Man Leung and Leung, Kwong Sak (2000). Data Mining Using Grammar Based Genetic Programming and Applications, volume 3 of Genetic Programming.

The field currently has no satisfying answer to this question, which will become all the more pressing as genetic programming systems incorporate more expressive and heterogeneous program representations. In the context of such representations it may well make sense for different program elements or program locations to have different variation rates or procedures, and it will not be obvious, in advance, how to make these choices. The question will also become all the more pressing as genetic programming systems are applied to ever more complex problems, about which the system designers will have less knowledge and intuition.

The JavaTM Series. Addison-Wesley, Boston, MA, USA, third edition. Harman, Mark (2010). Automated patching techniques: The fix is in. Communications of the ACM, 53(5):108. Kotzmann, Thomas, Wimmer, Christian, M¨ossenb¨ock, Hanspeter, Rodriguez, Thomas, Russell, Kenneth, and Cox, David (2008). Design of the Java HotSpotTM client compiler for Java 6. ACM Transactions on Architecture and Code Optimization, 5(1):7:1–32. Koza, John R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection.

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