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GPEM
2011
13 years 2 months ago
Expert-driven genetic algorithms for simulating evaluation functions
In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function’s parameters for computer chess. Our results show that using an appropr...
Omid David-Tabibi, Moshe Koppel, Nathan S. Netanya...
APIN
1998
139views more  APIN 1998»
13 years 7 months ago
Evolutionary Learning of Modular Neural Networks with Genetic Programming
Evolutionary design of neural networks has shown a great potential as a powerful optimization tool. However, most evolutionary neural networks have not taken advantage of the fact ...
Sung-Bae Cho, Katsunori Shimohara
GECCO
2003
Springer
14 years 23 days ago
Methods for Evolving Robust Programs
Many evolutionary computation search spaces require fitness assessment through the sampling of and generalization over a large set of possible cases as input. Such spaces seem par...
Liviu Panait, Sean Luke
EUROGP
2010
Springer
166views Optimization» more  EUROGP 2010»
14 years 20 days ago
Learning a Lot from Only a Little: Genetic Programming for Panel Segmentation on Sparse Sensory Evaluation Data
We describe a data mining framework that derives panelist information from sparse flavour survey data. One component of the framework executes genetic programming ensemble based s...
Katya Vladislavleva, Kalyan Veeramachaneni, Una-Ma...
EUROGP
2009
Springer
138views Optimization» more  EUROGP 2009»
14 years 2 months ago
Self Modifying Cartesian Genetic Programming: Fibonacci, Squares, Regression and Summing
Self Modifying CGP (SMCGP) is a developmental form of Cartesian Genetic Programming(CGP). It is able to modify its own phenotype during execution of the evolved program. This is do...
Simon Harding, Julian Francis Miller, Wolfgang Ban...