Runtime analysis of mutation-based geometric semantic genetic programming on boolean functions.
Moraglio, A; Mambrini, A; Manzoni, L
Date: 16 January 2013
Conference paper
Publisher
Association for Computing Machinery (ACM)
Publisher DOI
Abstract
Geometric Semantic Genetic Programming (GSGP) is a recently
introduced form of Genetic Programming (GP), rooted
in a geometric theory of representations, that searches directly
the semantic space of functions/programs, rather than
the space of their syntactic representations (e.g., trees) as in
traditional GP. Remarkably, the ...
Geometric Semantic Genetic Programming (GSGP) is a recently
introduced form of Genetic Programming (GP), rooted
in a geometric theory of representations, that searches directly
the semantic space of functions/programs, rather than
the space of their syntactic representations (e.g., trees) as in
traditional GP. Remarkably, the fitness landscape seen by
GSGP is always – for any domain and for any problem –
unimodal with a linear slope by construction. This has two
important consequences: (i) it makes the search for the optimum
much easier than for traditional GP; (ii) it opens the
way to analyse theoretically in a easy manner the optimisation
time of GSGP in a general setting. The runtime analysis
of GP has been very hard to tackle, and only simplified forms
of GP on specific, unrealistic problems have been studied so
far. We present a runtime analysis of GSGP with various
types of mutations on the class of all Boolean functions
Computer Science
Faculty of Environment, Science and Economy
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