- author = "Alberto Moraglio and Andrea Mambrini and Luca Manzoni",
- title = "Runtime Analysis of Mutation-Based Geometric Semantic Genetic Programming on Boolean Functions",
- booktitle = "Foundations of Genetic Algorithms",
- year = "2013",
- editor = "Frank Neumann and Kenneth {De Jong}",
- pages = "119--132",
- address = "Adelaide, Australia",
- month = "16-20 " # jan,
- organisation = "SigEvo",
- publisher = "ACM",
- keywords = "genetic algorithms, genetic programming, Boolean functions, geometric crossover, runtime analysis, semantics",
- isbn13 = "978-1-4503-1990-4",
-
URL = "
http://www.cs.bham.ac.uk/~axm322/pdf/gsgp_foga13.pdf",
-
URL = "
http://doi.acm.org/10.1145/2460239.2460251",
-
DOI = "
doi:10.1145/2460239.2460251",
- acmid = "2460251",
- size = "13 pages",
- 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 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 run time 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.",
-
notes = "Note change of author ordering. Also known as
\cite{Moraglio:2013:RAM:2460239.2460251}.
Jan 2013 gsgp_foga13.pdf is preprint http://www.sigevo.org/foga-2013/index.html",
