Computational Complexity Analysis of Genetic Programming - Initial Results and Future Directions

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@InCollection{Neumann:2011:GPTP,

• author = "Frank Neumann and Una-May O'Reilly and Markus Wagner",
• title = "Computational Complexity Analysis of Genetic Programming - Initial Results and Future Directions",
• booktitle = "Genetic Programming Theory and Practice IX",
• year = "2011",
• editor = "Rick Riolo and Ekaterina Vladislavleva and Jason H. Moore",
• series = "Genetic and Evolutionary Computation",
• address = "Ann Arbor, USA",
• month = "12-14 " # may,
• publisher = "Springer",
• chapter = "7",
• pages = "113--128",
• keywords = "genetic algorithms, genetic programming, Abstract Expression Grammars, Differential Evolution, Grammar Template Genetic, Programming, Particle Swarm, Symbolic Regression",
• isbn13 = "978-1-4614-1769-9",
• URL = "http://cs.adelaide.edu.au/~markus/pub/2011gptp.pdf",
• DOI = "doi:10.1007/978-1-4614-1770-5_7",
• abstract = "The computational complexity analysis of evolutionary algorithms working on binary strings has significantly increased the rigorous understanding on how these types of algorithm work. Similar results on the computational complexity of genetic programming would fill an important theoretic gap. They would significantly increase the theoretical understanding on how and why genetic programming algorithms work and indicate, in a rigorous manner, how design choices of algorithm components impact its success. We summarise initial computational complexity results for simple tree-based genetic programming and point out directions for future research.",
• notes = "part of \cite{Riolo:2011:GPTP}",
• affiliation = "School of Computer Science, University of Adelaide, Adelaide, Australia",

}

Genetic Programming entries for Frank Neumann Una-May O'Reilly Markus Wagner

Citations