- author = "David Jackson",
- title = "Single Node Genetic Programming on Problems with Side Effects",
- booktitle = "Parallel Problem Solving from Nature, PPSN XII (part 1)",
- year = "2012",
- editor = "Carlos A. {Coello Coello} and Vincenzo Cutello and Kalyanmoy Deb and Stephanie Forrest and Giuseppe Nicosia and Mario Pavone",
- volume = "7491",
- series = "Lecture Notes in Computer Science",
- pages = "327--336",
- address = "Taormina, Italy",
- month = sep # " 1-5",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, SNGP",
- isbn13 = "978-3-642-32936-4",
- bibsource = "DBLP, http://dblp.uni-trier.de",
-
DOI = "
doi:10.1007/978-3-642-32937-1_33",
- size = "10 pages",
- abstract = "Single Node Genetic Programming (SNGP) offers a new approach to GP in which every member of the population consists of just a single program node. Operands are formed from other members of the population, and evolution is driven by a hill-climbing approach using a single reversible operator. When the functions being used in the problem are free from side effects, it is possible to make use of a form of dynamic programming, which provides huge efficiency gains. In this research we turn our attention to the use of SNGP when the solution of problems relies on the presence of side effects. We demonstrate that SNGP can still be superior to conventional GP, and examine the role of evolutionary strategies in achieving this.",
-
notes = "Cited by \cite{kubalik2017enhanced}
Santa Fe artificial ant, Maze, Parse (Reverse Polish) stack",
- affiliation = "Dept. of Computer Science, University of Liverpool, Liverpool, L69 3BX United Kingdom",
