Advertisement

Behavioral Program Synthesis with Genetic Programming

  • Krzysztof¬†Krawiec

Part of the Studies in Computational Intelligence book series (SCI, volume 618)

Table of contents

  1. Front Matter
    Pages I-XXI
  2. Krzysztof Krawiec
    Pages 1-19
  3. Krzysztof Krawiec
    Pages 21-34
  4. Krzysztof Krawiec
    Pages 35-41
  5. Krzysztof Krawiec
    Pages 43-54
  6. Krzysztof Krawiec
    Pages 55-66
  7. Krzysztof Krawiec
    Pages 77-88
  8. Krzysztof Krawiec
    Pages 89-95
  9. Krzysztof Krawiec
    Pages 97-118
  10. Krzysztof Krawiec
    Pages 119-132
  11. Krzysztof Krawiec
    Pages 133-141
  12. Krzysztof Krawiec
    Pages 143-147
  13. Back Matter
    Pages 149-172

About this book

Introduction

Genetic programming (GP) is a popular heuristic methodology of program synthesis with origins in evolutionary computation. In this generate-and-test approach, candidate programs are iteratively produced and evaluated. The latter involves running programs on tests, where they exhibit complex behaviors reflected in changes of variables, registers, or memory. That behavior not only ultimately determines program output, but may also reveal its `hidden qualities' and important characteristics of the considered synthesis problem. However, the conventional GP is oblivious to most of that information and usually cares only about the number of tests passed by a program. This `evaluation bottleneck' leaves search algorithm underinformed about the actual and potential qualities of candidate programs.

 

This book proposes behavioral program synthesis, a conceptual framework that opens GP to detailed information on program behavior in order to make program synthesis more efficient. Several existing and novel mechanisms subscribing to that perspective to varying extent are presented and discussed, including implicit fitness sharing, semantic GP, co-solvability, trace convergence analysis, pattern-guided program synthesis, and behavioral archives of subprograms. The framework involves several concepts that are new to GP, including execution record, combined trace, and search driver, a generalization of objective function. Empirical evidence gathered in several presented experiments clearly demonstrates the usefulness of behavioral approach. The book contains also an extensive discussion of implications of the behavioral perspective for program synthesis and beyond.

 

Keywords

Behavioral Consistency Behavioral Program Synthesis Computational Intelligence Genetic Programming Intelligent Systems

Authors and affiliations

  • Krzysztof¬†Krawiec
    • 1
  1. 1.Institute of Computing SciencePoznan University of Technology Institute of Computing SciencePoznanPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-27565-9
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-27563-5
  • Online ISBN 978-3-319-27565-9
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site