Advances in Genetic Programming: v. 2 (Complex Adaptive Systems)

Advances in Genetic Programming: v. 2 (Complex Adaptive Systems)

By: Kenneth E. Kinnear (volume_editor), Peter J. Angeline (volume_editor)Hardback

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Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. The twenty-three contributions are divided into four parts: Variations on the Genetic Programming Theme; Hierarchical, Recursive, and Pruning Genetic Programs; Analysis and Implementation Issues; and New Environments for Genetic Programming.The first part extends the core concepts of genetic programming through the addition of new evolutionary techniques -- adaptive and self-adaptive crossover methods, hill climbing operators, and the inclusion of introns into the representation.Creating more concise executable structures is a long-term research topic in genetic programming. The second part describes the field's most recent efforts, including the dynamic manipulation of automatically defined functions, evolving logic programs that generate recursive structures, and using minimum description length heuristics to determine when and how to prune evolving structures.The third part takes up the many implementation and analysis issues associated with evolving programs. Advanced applications of genetic programming to nontrivial real-world problems are described in the final part: remote sensing of pressure ridges in Arctic sea ice formations from satellite imagery, economic prediction through model evolution, the evolutionary development of stress and loading models for novel materials, and data mining of a large customer database to optimize responses to special offers.

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Genetic programming's continued evolution, Peter J. Angeline. Part 1 Variations on the genetic programming theme: a comparative analysis of genetic programming, Una-May O'Reilly and Franz Oppacher; evolving programmers - the co-evolution of intelligent recombination operators, Astro Teller; extending genetic programming with recombinative guidance, Horishi Iba and Hugo de Garis; two self-adaptive crossover operators for genetic programming, Peter J. Angeline; explicitly defined introns and destructive crossover in genetic programming, Peter Nordin et al. Part 2 modular, recursive and pruning genetic programmes: simultaneous evolution of programmes and their control structures, Lee Spector; classifying protein segments as transmembrane domains - using architecture-altering operations in genetic programming, John R. Koza and David Andre; discovery of subroutines in genetic programming, Justinian P. Rosca and Dana H. Ballard; evolving recursive programmes for tree search, Scott Brave; evolving recursive functions for the even-parity problem using genetic programming, Man Leung Wong and Kwong Sak Leung; adaptive fitness functions for dynamic growing/pruning of programme trees, Byoung-Tak Zhang and Heinz Muhlenbein. Part 3 Analysis and implementation issues in genetic programming: efficiently representing populations in genetic programming, Maarten Keijzer; genetically optimizing the speed of programmes evolved to play tetris, Eric V. Siegel and Alexander D. Chaffee; the royal tree problem, a benchmark for single and multiple population genetic programming, William F. Punch et al; parallel genetic programming - a scalable implementation using the transputer network architcture, David Andre and John R. Koza; massively parallel genetic programming, Hugues Juille and Jordan B. Pollack; type inheritance in strongly typed genetic programming, Thomas D. Haynes et al; on using syntactic constraints with genetic programming, Frederic Gruau; data structures and genetic programming, William B. Langdon. Part 4 New environments for genetic programming: algorithm discovery using the genetic programming paradigm - extracting low-contrast curvilinear features from SAR images of Arctic ice, Jason M. Daida et al; genetic programming learning and the cobweb model, Shu-Heng Chen and Chia-Hsuan Yeh; evolutionary identification of macro-mechanical models, Marc Shoenauer et al; discovering time oriented abstractions in historical data to optimize decision tree classification, Brij Masand and Gregory Piatetsky-Shapiro. Part 5 Appendices: genetic programming resources on the World-Wide Web, Patrick Tufts; a bibliography for genetic programming, William B. Langdon.

Product Details

  • publication date: 03/12/1996
  • ISBN13: 9780262011587
  • Format: Hardback
  • Number Of Pages: 538
  • ID: 9780262011587
  • weight: 1021
  • ISBN10: 0262011581

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