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ICGA
1997
133views Optimization» more  ICGA 1997»
15 years 8 months ago
Messy Genetic Algorithms for Subset Feature Selection
Subset Feature Selection problems can have severalattributes which may make Messy Genetic Algorithms an appropriateoptimization method. First, competitive solutions may often use ...
L. Darrell Whitley, J. Ross Beveridge, Cesar Guerr...
CEC
2009
IEEE
16 years 1 months ago
Dynamic optimization using Self-Adaptive Differential Evolution
Abstract— In this paper we investigate a Self-Adaptive Differential Evolution algorithm (jDE) where F and CR control parameters are self-adapted and a multi-population method wit...
Janez Brest, Ales Zamuda, Borko Boskovic, Mirjam S...
NICSO
2010
Springer
15 years 10 months ago
Accelerated Genetic Algorithms with Markov Chains
t] Based on the mutation matrix formalism and past statistics of genetic algorithm, a Markov Chain transition probability matrix is introduced to provide a guided search for comple...
Guan Wang, Chen Chen, Kwok Yip Szeto
GECCO
2009
Springer
133views Optimization» more  GECCO 2009»
15 years 4 months ago
Improved analysis methods for crossover-based algorithms
We deepen the theoretical analysis of the genetic algorithm for the all-pairs shortest path problem proposed by Doerr, Happ and Klein (GECCO 2008). We show that the growth of the ...
Benjamin Doerr, Madeleine Theile
ANOR
2005
93views more  ANOR 2005»
15 years 6 months ago
Evolutionary Approaches to DNA Sequencing with Errors
In the paper, two evolutionary approaches to the general DNA sequencing problem, assuming both negative and positive errors in the spectrum, are compared. The older of them is base...
Jacek Blazewicz, Fred Glover, Marta Kasprzak