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GECCO
2003
Springer
137views Optimization» more  GECCO 2003»
15 years 10 months ago
The Spatially-Dispersed Genetic Algorithm
Abstract. Spatially structured population models improve the performance of genetic algorithms by assisting the selection scheme in maintaining diversity. A significant concern wi...
Grant Dick
155
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GECCO
2003
Springer
15 years 10 months ago
A Kernighan-Lin Local Improvement Heuristic That Solves Some Hard Problems in Genetic Algorithms
We present a Kernighan-Lin style local improvement heuristic for genetic algorithms. We analyze the run-time cost of the heuristic. We demonstrate through experiments that the heur...
William A. Greene
GECCO
2006
Springer
186views Optimization» more  GECCO 2006»
15 years 8 months ago
Genetic programming for agricultural purposes
Nitrogen is one of the most important chemical intakes to ensure the healthy growth of agricultural crops. However, some environmental concerns emerge (soil and water pollution) w...
Clément Chion, Luis E. Da Costa, Jacques-An...
GECCO
2006
Springer
213views Optimization» more  GECCO 2006»
15 years 8 months ago
Evolutionary unit testing of object-oriented software using strongly-typed genetic programming
Evolutionary algorithms have successfully been applied to software testing. Not only approaches that search for numeric test data for procedural test objects have been investigate...
Stefan Wappler, Joachim Wegener
GECCO
2008
Springer
238views Optimization» more  GECCO 2008»
15 years 6 months ago
Using multiple offspring sampling to guide genetic algorithms to solve permutation problems
The correct choice of an evolutionary algorithm, a genetic representation for the problem being solved (as well as their associated variation operators) and the appropriate values...
Antonio LaTorre, José Manuel Peña, V...