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GECCO
2004
Springer
14 years 1 months ago
How Are We Doing? Predicting Evolutionary Algorithm Performance
Abstract. Given an evolutionary algorithm for a problem and an instance of the problem, the results of several trials of the EA on the instance constitute a sample from the distrib...
Mark A. Renslow, Brenda Hinkemeyer, Bryant A. Juls...
HM
2009
Springer
145views Optimization» more  HM 2009»
14 years 7 days ago
A Hybrid Solver for Large Neighborhood Search: Mixing Gecode and EasyLocal + +
We present a hybrid solver (called GELATO) that exploits the potentiality of a Constraint Programming (CP) environment (Gecode) and of a Local Search (LS) framework (EasyLocal++ )....
Raffaele Cipriano, Luca Di Gaspero, Agostino Dovie...
ALGORITHMICA
2005
149views more  ALGORITHMICA 2005»
13 years 7 months ago
Approximating Maximum Weight Cycle Covers in Directed Graphs with Weights Zero and One
A cycle cover of a graph is a spanning subgraph each node of which is part of exactly one simple cycle. A k-cycle cover is a cycle cover where each cycle has length at least k. Gi...
Markus Bläser, Bodo Manthey
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...
GECCO
2008
Springer
168views Optimization» more  GECCO 2008»
13 years 8 months ago
Speed-up techniques for solving large-scale bTSP with the Two-Phase Pareto Local Search
We first present a method, called Two-Phase Pareto Local Search, to find a good approximation of the efficient set of the biobjective traveling salesman problem. In the first p...
Thibaut Lust