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» On the practical genetic algorithms
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EC
2002
228views ECommerce» more  EC 2002»
13 years 8 months ago
Improved Sampling of the Pareto-Front in Multiobjective Genetic Optimizations by Steady-State Evolution: A Pareto Converging Gen
Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we pre...
Rajeev Kumar, Peter Rockett
ACIIDS
2010
IEEE
171views Database» more  ACIIDS 2010»
13 years 11 months ago
Evolving Concurrent Petri Net Models of Epistasis
Abstract. A genetic algorithm is used to learn a non-deterministic Petri netbased model of non-linear gene interactions, or statistical epistasis. Petri nets are computational mode...
Michael Mayo, Lorenzo Beretta
JCIT
2008
108views more  JCIT 2008»
13 years 8 months ago
Data Mining for Genetics: A Genetic Algorithm Approach
MINING biological data is an emerging area of intersection between data mining and bioinformatics. Bio-informaticians have been working on the research and development of computat...
G. Madhu, Keshava Reddy
EMO
2001
Springer
109views Optimization» more  EMO 2001»
14 years 29 days ago
Specification of Genetic Search Directions in Cellular Multi-objective Genetic Algorithms
When we try to implement a multi-objective genetic algorithm (MOGA) with variable weights for finding a set of Pareto optimal solutions, one difficulty lies in determining appropri...
Tadahiko Murata, Hisao Ishibuchi, Mitsuo Gen