Visualising Evolutionary Search Spaces

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{McDermott:2014:sigevolution,

• author = "James McDermott",
• title = "Visualising Evolutionary Search Spaces",
• journal = "SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation",
• year = "2014",
• volume = "7",
• number = "1",
• pages = "2--10",
• month = aug,
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1931-8499",
• acmid = "2661736",
• publisher = "ACM",
• URL = "http://www.sigevolution.org/issues/SIGEVOlution0701.pdf",
• DOI = "doi:10.1145/2661735.2661736",
• code_url = "https://github.com/jmmcd/GPDistance",
• size = "9 pages",
• abstract = "Understanding the structure of search spaces can help us to design better search algorithms, and it is natural to try to understand search spaces by visualising them. For typical evolutionary search spaces, like the space of genetic programming trees, visualising them directly is impossible, because of their large dimensionality. However, we can use the idea of distances on search spaces to project them into two dimensions, expose their structure, and obtain useful and attractive visualisations.",

}

Genetic Programming entries for James McDermott

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