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Semantic Genetic Programming

Published: 11 July 2015 Publication History

Abstract

Semantic genetic programming is a recent, rapidly growing trend in Genetic Programming (GP) that aims at opening the 'black box' of the evaluation function and make explicit use of more information on program behavior in the search. In the most common scenario of evaluating a GP program on a set of input-output examples (fitness cases), the semantic approach characterizes program with a vector of outputs rather than a single scalar value (fitness). The past research on semantic GP has demonstrated that the additional information obtained in this way facilitates designing more effective search operators. In particular, exploiting the geometric properties of the resulting semantic space leads to search operators with attractive properties, which have provably better theoretical characteristics than conventional GP operators. This in turn leads to dramatic improvements in experimental comparisons.
The aim of the tutorial is to give a comprehensive overview of semantic methods in genetic programming, illustrate in an accessible way a formal geometric framework for program semantics to design provably good mutation and crossover operators for traditional GP problem domains, and to analyze rigorously their performance (runtime analysis). A number of real-world applications of this framework will be also presented. Other promising emerging approaches to semantics in GP will be reviewed. In particular, the recent developments in the behavioral programming, which aims at characterizing the entire program behavior (and not only program outputs) will be covered as well. Current challenges and future trends in semantic GP will be identified and discussed.
Selected methods and concepts will be accompanied with live software demonstrations. Also, efficient implementation of semantic search operators may be challenging. We will illustrate very efficient, concise and elegant implementations of these operators, which are available for download from the web.

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R. Ffrancon, M. Schoenauer, Greedy Semantic Local Search for Small Solutions, Semantic Methods in Genetic Programming Workshop, GECCO'15, 2015.

Cited By

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  • (2018)PSO-Based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic ProgrammingParallel Problem Solving from Nature – PPSN XV10.1007/978-3-319-99253-2_4(41-53)Online publication date: 22-Aug-2018
  • (2016)Surrogate Fitness via Factorization of Interaction MatrixGenetic Programming10.1007/978-3-319-30668-1_5(68-82)Online publication date: 24-Mar-2016

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cover image ACM Conferences
GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
July 2015
1568 pages
ISBN:9781450334884
DOI:10.1145/2739482
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 11 July 2015

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Author Tags

  1. genetic programming
  2. geometric crossover
  3. semantics

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GECCO '15
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Cited By

View all
  • (2018)PSO-Based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic ProgrammingParallel Problem Solving from Nature – PPSN XV10.1007/978-3-319-99253-2_4(41-53)Online publication date: 22-Aug-2018
  • (2016)Surrogate Fitness via Factorization of Interaction MatrixGenetic Programming10.1007/978-3-319-30668-1_5(68-82)Online publication date: 24-Mar-2016

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