Dynamic environments can speed up evolution with genetic programming
|Title:||Dynamic environments can speed up evolution with genetic programming||Authors:||O'Neill, Michael; Nicolau, Miguel; Brabazon, Anthony||Permanent link:||http://hdl.handle.net/10197/3571||Date:||2011||Online since:||2012-04-17T13:40:36Z||Abstract:||We present a study of dynamic environments with genetic programming to ascertain if a dynamic environment can speed up evolution when compared to an equivalent static environment. We present an analysis of the types of dynamic variation which can occur with a variable-length representation such as adopted in genetic programming identifying modular varying, structural varying and incremental varying goals. An empirical investigation comparing these three types of varying goals on dynamic symbolic regression benchmarks reveals an advantage for goals which vary in terms of increasing structural complexity. This provides evidence to support the added difficulty variable length representations incur due to their requirement to search structural and parametric space concurrently, and how directing search through varying structural goals with increasing complexity can speed up search with genetic programming.||Funding Details:||Science Foundation Ireland||Type of material:||Technical Report||Publisher:||University College Dublin. School of Computer Science and Informatics||Series/Report no.:||UCD School of Computer Science and Informatics Technical Report; UCD-CSI-2011-03||Copyright (published version):||2011 the author/owner(s)||Keywords:||Grammatical evolution; Genetic programming; Dynamic environments||Subject LCSH:||Genetic programming (Computer science)
|Other versions:||http://www.csi.ucd.ie/files/UCD-CSI-2011-03.pdf||Language:||en||Status of Item:||Not peer reviewed|
|Appears in Collections:||Computer Science Research Collection|
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