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» Short Term Unit-Commitment Using Genetic Algorithms
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SIGADA
2004
Springer
14 years 1 months ago
Comparative analysis of genetic algorithm implementations
Genetic Algorithms provide computational procedures that are modeled on natural genetic system mechanics, whereby a coded solution is “evolved” from a set of potential solutio...
Robert Soricone, Melvin Neville
GECCO
2008
Springer
159views Optimization» more  GECCO 2008»
13 years 8 months ago
IGAP: interactive genetic algorithm peer to peer
We present IGAP, a peer to peer interactive genetic algorithm which reflects the real world methodology followed in team design. We apply our methodology to floorplanning. Throu...
Juan C. Quiroz, Amit Banerjee, Sushil J. Louis
GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
14 years 2 months ago
Three interconnected parameters for genetic algorithms
When an optimization problem is encoded using genetic algorithms, one must address issues of population size, crossover and mutation operators and probabilities, stopping criteria...
Pedro A. Diaz-Gomez, Dean F. Hougen
GECCO
2008
Springer
135views Optimization» more  GECCO 2008»
13 years 8 months ago
Evolving sequence patterns for prediction of sub-cellular locations of eukaryotic proteins
A genetic algorithm (GA) is utilised to discover known and novel PROSITE-like sequence templates that can be used to classify the sub-cellular location of eukaryotic proteins. Whi...
Greg Paperin
CAINE
2003
13 years 9 months ago
A Genetic Algorithm for Clustering on Very Large Data Sets
Clustering is the process of subdividing an input data set into a desired number of subgroups so that members of the same subgroup are similar and members of different subgroups h...
Jim Gasvoda, Qin Ding