An Efficient Implementation of GSGP using Higher-Order Functions and Memoization

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Moraglio:2014:SMGP2,

• author = "Alberto Moraglio",
• title = "An Efficient Implementation of GSGP using Higher-Order Functions and Memoization",
• booktitle = "Semantic Methods in Genetic Programming",
• year = "2014",
• editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill",
• address = "Ljubljana, Slovenia",
• month = "13 " # sep,
• note = "Workshop at Parallel Problem Solving from Nature 2014 conference",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Moraglio2.pdf",
• size = "2 pages",
• abstract = "Geometric Semantic Genetic Programming (GSGP) [1] is a novel form of Genetic Programming (GP) that can be interpreted as searching directly the semantic space of programs. This new form of GP is very promising as it induces always a simple unimodal fitness landscape for any problem it is applied to, hence it converges to the optimum very quickly. A drawback of GSGP with crossover is the exponential growth of individuals due to the fact that the offspring tree contains both parent trees, hence individuals double their size at each generation. Vanneschi et al. [2] have proposed an implementation of GSGP with crossover using a complex pointer-based data structure that prevents the exponential growth by keeping trace of the ancestry of individuals rather than storing them directly. We propose a new implementation of GSGP also based on tracing the ancestry of individuals, that however does not explicitly build and maintain a data structure, but uses higher-order functions and memoization to achieve the same effect, leaving the burden of book-keeping to the compiler. The resulting implementation is fast, elegant and concise. A Python implementation (under 100 lines without comments) is on GitHub at https://github.com/amoraglio/GSGP.",
• notes = "SMGP 2014",

}

Genetic Programming entries for Alberto Moraglio

Citations