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Graph Structured Program Evolution: Evolution of Loop Structures

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Genetic Programming Theory and Practice VII

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

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Abstract

Recently, numerous automatic programming techniques have been developed and applied in various fields. A typical example is genetic programming (GP), and various extensions and representations of GP have been proposed thus far. Complex programs and hand-written programs, however, may contain several loops and handle multiple data types. In this chapter, we propose a new method called Graph Structured Program Evolution (GRAPE). The representation of GRAPE is a graph structure; therefore, it can represent branches and loops using this structure. Each programis constructed as an arbitrary directed graph of nodes and a data set. The GRAPE program handles multiple data types using the data set for each type, and the genotype of GRAPE takes the form of a linear string of integers. We apply GRAPE to three test problems, factorial, exponentiation, and list sorting, and demonstrate that the optimum solution in each problem is obtained by the GRAPE system.

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Authors and Affiliations

  1. Graduate School of Environment and Information Sciences, Yokohama National University, 79-7, Tokiwadai, Hodogaya-ku, Yokohama, Kanagawa, 240-8501, Japan

    Shinichi Shirakawa & Tomoharu Nagao

Authors
  1. Shinichi Shirakawa
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  2. Tomoharu Nagao
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Editor information

Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, West Hall 323, Ann Arbor, 48109, U.S.A.

    Rick Riolo

  2. Computer Science &, Massachusetts Institute of Technology, Vassar St. 32, Cambridge, 02139, U.S.A.

    Una-May O'Reilly

  3. Solido Design Automation, Inc., Research Drive 102-116, Saskatoon, S7N 3R3, Canada

    Trent McConaghy

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Shirakawa, S., Nagao, T. (2010). Graph Structured Program Evolution: Evolution of Loop Structures. In: Riolo, R., O'Reilly, UM., McConaghy, T. (eds) Genetic Programming Theory and Practice VII. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1626-6_11

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  • DOI: https://doi.org/10.1007/978-1-4419-1626-6_11

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-1653-2

  • Online ISBN: 978-1-4419-1626-6

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