The Evolutionary Transition Algorithm: Evolving Complex Solutions out of Simpler Ones (bibtex)
@incollection{LDH2009,
	_day = {13},
	_month = {11},
	abstract = {Capturing the metaphor of evolutionary transitions in biological complexity, the Evolutionary Transition Algorithm (ETA) evolves solutions of increasing structural and functional complexity from the symbiotic interaction of partial ones. In this chapter we show that the ETA indeed captures this idea and we illustrate this on instances of the Binary Constraint Satisfaction problem. The results make the ETA a promising optimization approach that requires more extensive investigation from both a theoretical and optimization perspective. We analyze here, in depth, some of the design choices that are made for the algorithm. The analysis of these choices provides insight on the plasticity of the algorithm toward alternative choices and other kinds of problems. },
	author = {T. Lenaerts and A. Defaweux and {van Hemert}, J.I.},
	booktitle = {Nature-Inspired Algorithms for Optimisation},
	date-added = {2008-11-13 13:30:06 +0000},
	date-modified = {2010-01-18 15:56:00 +0000},
	editor = {Raymond Chiong},
	keywords = {constraint satisfaction; evolutionary computation},
	pages = {103--131},
	publisher = {Springer},
	series = {Studies in Computational Intelligence},
	title = {The Evolutionary Transition Algorithm: Evolving Complex Solutions out of Simpler Ones},
	volume = {193},
	year = {2009}}
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