Genetic Programming Theory and Practice XI

1. Front Matter

   Pages i-xiv

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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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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.

• Artificial evolution
• Evolution of models
• Feature selection
• Genetic programming
• Genetic programming applications
• Genetic programming theory
• Program induction
• Symbolic regression
• algorithm analysis and problem complexity

“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)

• 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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