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CORR
2006
Springer
153views Education» more  CORR 2006»
13 years 7 months ago
Genetic Programming, Validation Sets, and Parsimony Pressure
Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a limited n...
Christian Gagné, Marc Schoenauer, Marc Pari...
GECCO
2010
Springer
189views Optimization» more  GECCO 2010»
14 years 13 days ago
Knowledge mining with genetic programming methods for variable selection in flavor design
This paper presents a novel approach for knowledge mining from a sparse and repeated measures dataset. Genetic programming based symbolic regression is employed to generate multip...
Katya Vladislavleva, Kalyan Veeramachaneni, Matt B...
CEC
2010
IEEE
13 years 5 months ago
Active Learning Genetic programming for record deduplication
The great majority of genetic programming (GP) algorithms that deal with the classification problem follow a supervised approach, i.e., they consider that all fitness cases availab...
Junio de Freitas, Gisele L. Pappa, Altigran Soares...
CEC
2009
IEEE
14 years 2 months ago
Optimization of the sizing of a solar thermal electricity plant: Mathematical programming versus genetic algorithms
— Genetic algorithms (GAs) have been argued to constitute a flexible search thereby enabling to solve difficult problems which classical optimization methodologies may find ha...
Jose M. Cabello, Jose M. Cejudo, Mariano Luque, Fr...
GECCO
2006
Springer
176views Optimization» more  GECCO 2006»
13 years 11 months ago
A genetic algorithm with backtracking for protein structure prediction
In this paper, we propose a simple genetic algorithm for finding the optimal conformation of a protein using the three-dimensional square HP model. A backtracking procedure is use...
Clayton Matthew Johnson, Anitha Katikireddy