Dynamic environments can speed up evolution with genetic programming

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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
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
Copyright (published version): 2011 the author/owner(s)
Keywords: Grammatical evolution;Genetic programming;Dynamic environments
Subject LCSH: Genetic programming (Computer science)
Evolutionary computation
Language: en
Status of Item: Not peer reviewed
Appears in Collections:Computer Science Research Collection
CASL Research Collection

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