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GECCO
2006
Springer
157views Optimization» more  GECCO 2006»
14 years 2 months ago
gLINC: identifying composability using group perturbation
We present two novel perturbation-based linkage learning algorithms that extend LINC [5]; a version of LINC optimised for decomposition tasks (oLINC) and a hierarchical version of...
David Jonathan Coffin, Christopher D. Clack
GECCO
2006
Springer
202views Optimization» more  GECCO 2006»
14 years 2 months ago
Evolving hash functions by means of genetic programming
The design of hash functions by means of evolutionary computation is a relatively new and unexplored problem. In this work, we use Genetic Programming (GP) to evolve robust and fa...
César Estébanez, Julio César ...
GECCO
2006
Springer
156views Optimization» more  GECCO 2006»
14 years 2 months ago
Improving GP classifier generalization using a cluster separation metric
Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited...
Ashley George, Malcolm I. Heywood
GECCO
2006
Springer
282views Optimization» more  GECCO 2006»
14 years 2 months ago
A genetic algorithm for the longest common subsequence problem
A genetic algorithm for the longest common subsequence problem encodes candidate sequences as binary strings that indicate subsequences of the shortest or first string. Its fitnes...
Brenda Hinkemeyer, Bryant A. Julstrom
GECCO
2006
Springer
167views Optimization» more  GECCO 2006»
14 years 2 months ago
Genomic computing networks learn complex POMDPs
A genomic computing network is a variant of a neural network for which a genome encodes all aspects, both structural and functional, of the network. The genome is evolved by a gen...
David J. Montana, Eric Van Wyk, Marshall Brinn, Jo...