Crossover Context in Page-based Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{wilson:2003:ccpb,

• author = "G. C. Wilson and M. I. Heywood",
• title = "Crossover Context in Page-based Linear Genetic Programming",
• booktitle = "IEEE CCECE 2002: IEEE Canadian Conference on Electrical and Computer Engineering",
• year = "2002",
• editor = "W. Kinsner and A. Seback and K. Ferens",
• pages = "809--814",
• volume = "2",
• month = "12-15 " # may,
• organisation = "IEEE Canada",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, Strategy Learning, learning (artificial intelligence), search problems, San Mateo trail, artificial ants, code sequences, crossover operator, effective search strategies, fitness change, instructions, simple register based memories, strategy learning",
• ISBN = "0-7803-7515-7",
• ISSN = "0840-7789",
• URL = "http://flame.cs.dal.ca/~gwilson/docs/papers/ccece_2002.pdf",
• DOI = "doi:10.1109/CCECE.2002.1013046",
• size = "6 pages",
• abstract = "This work explores strategy learning through genetic programming in artificial ants that navigate the San Mateo trail. We investigate several properties of linearly structured (as opposed to typical tree based) GP including: the significance of simple register based memories, the significance of constraints applied to the crossover operator, and how active the ant are. We also provide a basis for investigating more thoroughly the relation between specific code sequences and fitness by dividing the genome into pages of instructions and introducing an analysis of fitness change and exploration of the trail done by particular parts of a genome. By doing so we are able to present results on how best to find the instructions in an individual's program that contribute positively to the accumulation of effective search strategies.",
• notes = "best student paper award",

}

Genetic Programming entries for Garnett Carl Wilson Malcolm Heywood

Citations