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GECCO
2008
Springer
172views Optimization» more  GECCO 2008»
15 years 2 months ago
Empirical analysis of a genetic algorithm-based stress test technique
Evolutionary testing denotes the use of evolutionary algorithms, e.g., Genetic Algorithms (GAs), to support various test automation tasks. Since evolutionary algorithms are heuris...
Vahid Garousi
GECCO
2008
Springer
165views Optimization» more  GECCO 2008»
15 years 2 months ago
Dual-population genetic algorithm for nonstationary optimization
In order to solve nonstationary optimization problems efficiently, evolutionary algorithms need sufficient diversity to adapt to environmental changes. The dual-population genetic...
Taejin Park, Ri Choe, Kwang Ryel Ryu
CEC
2009
IEEE
15 years 8 months ago
Representation and structural biases in CGP
— An evolutionary algorithm automatically discovers suitable solutions to a problem, which may lie anywhere in a large search space of candidate solutions. In the case of Genetic...
Andrew J. Payne, Susan Stepney
AUSAI
1999
Springer
15 years 5 months ago
Applying Ant Algorithms and the No Fit Polygon to the Nesting Problem
In previous work solutions for the nesting problem are produced using the no fit polygon (NFP), a new evaluation method and three evolutionary algorithms (simulated annealing (SA),...
Edmund K. Burke, Graham Kendall
CEC
2007
IEEE
15 years 1 months ago
Designing memetic algorithms for real-world applications using self-imposed constraints
— Memetic algorithms (MAs) combine the global exploration abilities of evolutionary algorithms with a local search to further improve the solutions. While a neighborhood can be e...
Thomas Michelitsch, Tobias Wagner, Dirk Biermann, ...