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CHINAF
2011
14 years 10 months ago
Immigrant schemes for evolutionary algorithms in dynamic environments: Adapting the replacement rate
One approach for evolutionary algorithms (EAs) to address dynamic optimization problems (DOPs) is to maintain diversity of the population via introducing immigrants. So far all imm...
Xin Yu, Ke Tang, Xin Yao
KDD
2005
ACM
151views Data Mining» more  KDD 2005»
16 years 7 months ago
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text information collected over time. Since most text information bears some time stamps, TTM has man...
Qiaozhu Mei, ChengXiang Zhai
CEC
2007
IEEE
16 years 1 months ago
Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods
— Hypervolume based multiobjective evolutionary algorithms (MOEA) nowadays seem to be the first choice when handling multiobjective optimization problems with many, i.e., at lea...
Dimo Brockhoff, Eckart Zitzler
EPIA
2007
Springer
16 years 29 days ago
Improving Evolutionary Algorithms with Scouting
The goal of an Evolutionary Algorithm(EA) is to find the optimal solution to a given problem by evolving a set of initial potential solutions. When the problem is multi-modal, an ...
Konstantinos Bousmalis, Gillian M. Hayes, Jeffrey ...
GECCO
2007
Springer
276views Optimization» more  GECCO 2007»
16 years 29 days ago
Meta-optimizing semantic evolutionary search
I present MOSES (meta-optimizing semantic evolutionary search), a new probabilistic modeling (estimation of distribution) approach to program evolution. Distributions are not esti...
Moshe Looks