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» Rank based variation operators for genetic algorithms
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EMO
2001
Springer
109views Optimization» more  EMO 2001»
14 years 3 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
JNW
2006
88views more  JNW 2006»
13 years 7 months ago
A Genetic Algorithms Based Approach for Group Multicast Routing
Whereas multicast transmission in one-to-many communications allows the operator to drastically save network resources, it also makes the routing of the traffic flows more complex ...
Luca Sanna Randaccio, Luigi Atzori
GECCO
1999
Springer
126views Optimization» more  GECCO 1999»
13 years 12 months ago
Improving Genetic Algorithms by Search Space Reductions (with Applications to Flow Shop Scheduling)
Crossover operators that preserve common components can also preserve representation level constraints. Consequently, these constraints can be used to beneficially reduce the sea...
Stephen Y. Chen, Stephen F. Smith
GECCO
2005
Springer
157views Optimization» more  GECCO 2005»
14 years 1 months ago
Simple addition of ranking method for constrained optimization in evolutionary algorithms
During the optimization of a constrained problem using evolutionary algorithms (EAs), an individual in the population can be described using three important properties, i.e., obje...
Pei Yee Ho, Kazuyuki Shimizu
GECCO
2005
Springer
152views Optimization» more  GECCO 2005»
14 years 1 months ago
A multi-objective genetic algorithm for robust design optimization
Real-world multi-objective engineering design optimization problems often have parameters with uncontrollable variations. The aim of solving such problems is to obtain solutions t...
Mian Li, Shapour Azarm, Vikrant Aute