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GECCO
2005
Springer
154views Optimization» more  GECCO 2005»
14 years 1 months ago
Genetic algorithms using low-discrepancy sequences
The random number generator is one of the important components of evolutionary algorithms (EAs). Therefore, when we try to solve function optimization problems using EAs, we must ...
Shuhei Kimura, Koki Matsumura
KDD
2012
ACM
194views Data Mining» more  KDD 2012»
11 years 10 months ago
A sparsity-inducing formulation for evolutionary co-clustering
Traditional co-clustering methods identify block structures from static data matrices. However, the data matrices in many applications are dynamic; that is, they evolve smoothly o...
Shuiwang Ji, Wenlu Zhang, Jun Liu
GECCO
2006
Springer
130views Optimization» more  GECCO 2006»
13 years 11 months ago
Pairwise sequence comparison for fitness evaluation in evolutionary structural software testing
Evolutionary algorithms are among the metaheuristic search methods that have been applied to the structural test data generation problem. Fitness evaluation methods play an import...
H. Turgut Uyar, A. Sima Etaner-Uyar, A. Emre Harma...
AEI
2005
99views more  AEI 2005»
13 years 7 months ago
Comparison among five evolutionary-based optimization algorithms
Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed ...
Emad Elbeltagi, Tarek Hegazy, Donald E. Grierson
KBS
2006
87views more  KBS 2006»
13 years 7 months ago
Predictive and comprehensible rule discovery using a multi-objective genetic algorithm
We present a multi-objective genetic algorithm for mining highly predictive and comprehensible classification rules from large databases. We emphasize predictive accuracy and comp...
Satchidananda Dehuri, Rajib Mall