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Genetic Programming Theory and Practice XI

  • Book
  • © 2014

Overview

Editors:
  1. Rick Riolo

    1. University of Michigan, Ann Arbor, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Jason H. Moore

    1. Inst for Quantitative Biomedical Science, Dartmouth Medical School, Lebanon, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Mark Kotanchek

    1. Evolved Analytics, Midland, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Describes cutting-edge work on genetic programming (GP) theory, applications of GP and how theory can be used to guide application of GP
  • Demonstrates large-scale applications of GP to a variety of problem domains
  • Reveals an inspiring synergy between GP applications and the latest in theoretical results for state-of –the-art problem solving
  • Includes supplementary material: sn.pub/extras

Part of the book series: Genetic and Evolutionary Computation (GEVO)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xiv
    Download chapter PDF

  2. Extreme Accuracy in Symbolic Regression

    • Michael F. Korns
    Pages 1-30

  3. Exploring Interestingness in a Computational Evolution System for the Genome-Wide Genetic Analysis of Alzheimer’s Disease

    • Jason H. Moore, Douglas P. Hill, Andrew Saykin, Li Shen
    Pages 31-45

  4. Optimizing a Cloud Contract Portfolio Using Genetic Programming-Based Load Models

    • Sean Stijven, Ruben Van den Bossche, Ekaterina Vladislavleva, Kurt Vanmechelen, Jan Broeckhove, Mark Kotanchek
    Pages 47-63

  5. Maintenance of a Long Running Distributed Genetic Programming System for Solving Problems Requiring Big Data

    • Babak Hodjat, Erik Hemberg, Hormoz Shahrzad, Una-May O’Reilly
    Pages 65-83

  6. Grounded Simulation: Using Simulated Evolution to Guide Embodied Evolution

    • Conor Ryan, Joe Sullivan, Barry Fitzgerald
    Pages 85-100

  7. Applying Genetic Programming in Business Forecasting

    • Arthur K. Kordon
    Pages 101-117

  8. Explaining Unemployment Rates with Symbolic Regression

    • Philip Truscott, Michael F. Korns
    Pages 119-135

  9. Uniform Linear Transformation with Repair and Alternation in Genetic Programming

    • Lee Spector, Thomas Helmuth
    Pages 137-153

  10. A Deterministic and Symbolic Regression Hybrid Applied to Resting-State fMRI Data

    • Ilknur Icke, Nicholas A. Allgaier, Christopher M. Danforth, Robert A. Whelan, Hugh P. Garavan, Joshua C. Bongard
    Pages 155-173

  11. Gaining Deeper Insights in Symbolic Regression

    • Michael Affenzeller, Stephan M. Winkler, Gabriel Kronberger, Michael Kommenda, Bogdan Burlacu, Stefan Wagner
    Pages 175-190

  12. Geometric Semantic Genetic Programming for Real Life Applications

    • Leonardo Vanneschi, Sara Silva, Mauro Castelli, Luca Manzoni
    Pages 191-209

  13. Evaluation of Parameter Contribution to Neural Network Size and Fitness in ATHENA for Genetic Analysis

    • Ruowang Li, Emily R. Holzinger, Scott M. Dudek, Marylyn D. Ritchie
    Pages 211-224

  14. Back Matter

    Pages 225-227
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Keywords

About this book

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: evolutionary constraints, relaxation of selection mechanisms, diversity preservation strategies, flexing fitness evaluation, evolution in dynamic environments, multi-objective and multi-modal selection, foundations of evolvability, evolvable and adaptive evolutionary operators, foundation of injecting expert knowledge in evolutionary search, analysis of problem difficulty and required GP algorithm complexity, foundations in running GP on the cloud – communication, cooperation, flexible implementation, and ensemble methods. Additional focal points for GP symbolic regression are: (1) The need to guarantee 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 different types of data. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.

Reviews

“This volume is a collection of 12 papers … authored by leading theorists and practitioners of GP, and submitted for the Genetic Programming Theory and Practice (GPTP) workshop held at the University of Michigan on May 9-11, 2013. This collection will interest GP researchers and practitioners with sufficient background in artificial intelligence, evolved analytics, and smart systems.” (Anoop Malaviya, Computing Reviews, December, 2015)

Editors and Affiliations

  • University of Michigan, Ann Arbor, USA

    Rick Riolo

  • Inst for Quantitative Biomedical Science, Dartmouth Medical School, Lebanon, USA

    Jason H. Moore

  • Evolved Analytics, Midland, USA

    Mark Kotanchek

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