Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Parallel Problem Solving from Nature, 2: Proceedings of the Second Conference on Parallel Problem Solving from Nature, Brussels, Belgium, 28-30 September, 1992
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Bin Packing with Adaptive Search
Proceedings of the 1st International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A grouping genetic algorithm for coloring the edges of graphs
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
A Genetic Algorithm for the Group-Technology Problem
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Hybrid Genetic Algorithm for Solving the p-Median Problem
SEAL'98 Selected papers from the Second Asia-Pacific Conference on Simulated Evolution and Learning on Simulated Evolution and Learning
To combine steady-state genetic algorithm and ensemble learning for data clustering
Pattern Recognition Letters
Evolutionary based heuristic for bin packing problem
Computers and Industrial Engineering
Computers and Operations Research
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
A metaheuristic for the fixed job scheduling problem under spread time constraints
Computers and Operations Research
Combining multiple representations in a genetic algorithm for the multiple Knapsack problem
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Incremental semi-supervised clustering in a data stream with a flock of agents
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
A new grouping genetic algorithm for the quadratic multiple knapsack problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
A review: accuracy optimization in clustering ensembles using genetic algorithms
Artificial Intelligence Review
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
FDClust: a new bio-inspired divisive clustering algorithm
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Formal analysis, hardness, and algorithms for extracting internal structure of test-based problems
Evolutionary Computation
Application of the grouping genetic algorithm to university course timetabling
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Using datamining techniques to help metaheuristics: a short survey
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Evolutionary Algorithm for Economic lot and Delivery Scheduling Problem
Fundamenta Informaticae
The Gestalt heuristic: emerging abstraction to improve combinatorial search
Natural Computing: an international journal
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Study of Sensitive Parameters of PSO Application to Clustering of Texts
International Journal of Applied Evolutionary Computation
A particle swarm optimizer for grouping problems
Information Sciences: an International Journal
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An important class of computational problems are grouping problems, where the aim is to group together members of a set (i.e., find a good partition of the set). We show why both the standard and the ordering GAs fare poorly in this domain by pointing out their inherent difficulty to capture the regularities of the functional landscape of the grouping problems. We then propose a new encoding scheme and genetic operators adapted to these problems, yielding the Grouping Genetic Algorithm (GGA). We give an experimental comparison of the GGA with the other GAs applied to grouping problems, and we illustrate the approach with two more examples of important grouping problems successfully treated with the GGA: the problems of Bin Packing and Economies of Scale.