Probabilistic Graph Programs for Randomised and Evolutionary Algorithms

Timothy Atkinson, Detlef Plump, Susan Stepney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We extend the graph programming language GP 2 with probabilistic constructs: (1) choosing rules according to user-defined probabilities and (2) choosing rule matches uniformly at random. We demonstrate these features with graph programs for randomised and evolutionary algorithms. First, we implement Karger’s minimum cut algorithm, which contracts randomly selected edges; the program finds a minimum cut with high probability. Second, we generate random graphs according to the G(n, p) model. Third, we apply probabilistic graph programming to evolutionary algorithms working on graphs; we benchmark odd-parity digital circuit problems and show that our approach significantly outperforms the established approach of Cartesian Genetic Programming.

Original languageEnglish
Title of host publicationGraph Transformation - 11th International Conference, ICGT 2018, Held as Part of STAF 2018, Proceedings
EditorsJens Weber, Leen Lambers
PublisherSpringer
Pages63-78
Number of pages16
ISBN (Electronic)978-3-319-92991-0
ISBN (Print)978-3-319-92990-3
DOIs
Publication statusPublished - 19 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10887 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

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