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» Unsupervised Problem Decomposition Using Genetic Programming
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GECCO
2003
Springer
15 years 2 months ago
Enhancing the Performance of GP Using an Ancestry-Based Mate Selection Scheme
The performance of genetic programming relies mostly on population-contained variation. If the population diversity is low then there will be a greater chance of the algorithm bein...
Rodney Fry, Andrew M. Tyrrell
ENTCS
2006
113views more  ENTCS 2006»
14 years 9 months ago
Concurrent Java Test Generation as a Search Problem
A Random test generator generates executable tests together with their expected results. In the form of a noise-maker, it seeds the program with conditional scheduling primitives ...
Yaniv Eytani
NC
1998
138views Neural Networks» more  NC 1998»
14 years 11 months ago
Parallel Adaptive Genetic Algorithm
In this paper we introduce an efficient implementation of asynchronously parallel genetic algorithm with adaptive genetic operators. The classic genetic algorithm paradigm is exte...
Leo Budin, Marin Golub, Domagoj Jakobovic
CP
2009
Springer
15 years 4 months ago
Why Cumulative Decomposition Is Not as Bad as It Sounds
Abstract. The global cumulative constraint was proposed for modelling cumulative resources in scheduling problems for finite domain (FD) propagation. Since that time a great deal ...
Andreas Schutt, Thibaut Feydy, Peter J. Stuckey, M...
ECAI
2010
Springer
14 years 10 months ago
Nested Monte-Carlo Expression Discovery
Nested Monte-Carlo search is a general algorithm that gives good results in single player games. Genetic Programming evaluates and combines trees to discover expressions that maxim...
Tristan Cazenave