Tracking Extrema in Dynamic Environments

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

@InProceedings{angeline:1997:txde,

• author = "Peter J. Angeline",
• title = "Tracking Extrema in Dynamic Environments",
• booktitle = "Proceedings of the 6th International Conference on Evolutionary Programming",
• year = "1997",
• editor = "P. J. Angeline and R. G. Reynolds and J. R. McDonnell and R. Eberhart",
• volume = "1213",
• series = "Lecture Notes in Computer Science",
• pages = "335--345",
• address = "Indianapolis, Indiana, USA",
• month = apr # " 13-16",
• publisher = "Springer Verlag",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-62788-X",
• URL = "http://www.natural-selection.com/Library/1997/ep97b.pdf",
• DOI = "doi:10.1007/BFb0014823",
• size = "11 pages",
• abstract = "Typical applications of evolutionary optimization involve the off-line approximation of extrema of static multi-modal functions. Methods which use a variety of techniques to self-adapt mutation parameters have been shown to be more successful than methods which do not use self-adaptation. For dynamic functions, the interest is not to obtain the extrema but to follow it as closely as possible. This paper compares the on-line extrema tracking performance of an evolutionary program without self-adaptation against an evolutionary program using a self-adaptive Gaussian update rule over a number of dynamics applied to a simple static function. The experiments demonstrate that for some dynamic functions, self-adaptation is effective while for others it is detrimental.",
• notes = "EP-97",

}

Genetic Programming entries for Peter John Angeline

Citations