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Deriving genetic programming fitness properties by static analysis

Johnson, Colin G. (2002) Deriving genetic programming fitness properties by static analysis. In: Lutton, Evelyne and Foster, James A. and Miller, Julian and Ryan, Conor and Tettamanzi, Andrea, eds. Genetic Programming: 5th European Conference. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 299-308. ISBN 978-3-540-43378-1. E-ISBN 978-3-540-45984-2. (doi:10.1007/3-540-45984-7_29) (KAR id:13804)


Deriving Genetic Programming Fitness Properties by Static Analysis Colin G. Johnson The aim of this paper is to introduce the idea of using static analysis of computer programs as a way of measuring fitness in genetic programming. Such techniques extract information about the programs without explicitly running them, and in particular they infer properties which hold across the whole of the input space of a program. This can be applied to measure fitness, and has a number of advantages over measuring fitness by running members of the population on test cases. The most important advantage is that if a solution is found then it is possible to formally trust that solution to be correct across all inputs. This paper introduces these ideas, discusses various ways in which they could be applied, discusses the type of problems for which they are appropriate, and ends by giving a simple test example and some questions for future research.

Item Type: Book section
DOI/Identification number: 10.1007/3-540-45984-7_29
Uncontrolled keywords: genetic algorithms, genetic programming, program analysis
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:00 UTC
Last Modified: 16 Nov 2021 09:51 UTC
Resource URI: (The current URI for this page, for reference purposes)

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