Search, polynomial complexity, and the fast messy genetic algorithm
Search, polynomial complexity, and the fast messy genetic algorithm
A Constructive Evolutionary Approach to School Timetabling
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics, Vol 57)
Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics, Vol 57)
Constructive Genetic Algorithm for Clustering Problems
Evolutionary Computation
A new hybrid heuristic for driver scheduling
International Journal of Hybrid Intelligent Systems - VIII Brazilian Symposium On Neural Networks
A hybrid column generation approach for the berth allocation problem
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Population training heuristics
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
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This paper describes an application of a Constructive Genetic Algorithm (CGA) to the Minimization Open Stack Problem (MOSP). The MOSP happens in a production system scenario, and consists of determining a sequence of cut patterns that minimizes the maximum number of opened stacks during the cutting process. The CGA has a number of new features compared to a traditional genetic algorithm, as a population of dynamic size composed of schemata and structures that is trained with respect to some problem specific heuristic. The application of CGA to MOSP uses a 2-Opt like heuristic to define the fitness functions and the mutation operator. Computational tests are presented using available instances taken from the literature.