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GECCO
2003
Springer
14 years 20 days 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
2008
Springer
238views Optimization» more  GECCO 2008»
13 years 8 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...
EUSFLAT
2009
195views Fuzzy Logic» more  EUSFLAT 2009»
13 years 5 months ago
Optimization of an Oil Production System using Neural Networks and Genetic Algorithms
This paper proposes an optimization strategy which is based on neural networks and genetic algorithms to calculate the optimal values of gas injection rate and oil rate for oil pro...
Guillermo Jimenez de la Cruz, Jose A. Ruz-Hernande...
GECCO
2007
Springer
186views Optimization» more  GECCO 2007»
14 years 1 months ago
A multi-objective approach for the prediction of loan defaults
Credit institutions are seldom faced with problems dealing with single objectives. Often, decisions involving optimizing two or more competing goals simultaneously need to be made...
Oluwarotimi Odeh, Praveen Koduru, Sanjoy Das, Alle...
GECCO
2003
Springer
148views Optimization» more  GECCO 2003»
14 years 20 days ago
Ant Colony Programming for Approximation Problems
Abstract. A method of automatic programming, called genetic programming, assumes that the desired program is found by using a genetic algorithm. We propose an idea of ant colony pr...
Mariusz Boryczka, Zbigniew J. Czech, Wojciech Wiec...