A method for solving to optimality uncapacitated location problems
Annals of Operations Research
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Bionomic Approach to the Capacitated p-Median Problem
Journal of Heuristics
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A Constructive Evolutionary Approach to School Timetabling
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
-Opt Population Training for Minimization of Open Stack Problem
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
A column generation approach to capacitated p-median problems
Computers and Operations Research
The capacitated centred clustering problem
Computers and Operations Research
Proportionate flexible flow shop scheduling via a hybrid constructive genetic algorithm
Expert Systems with Applications: An International Journal
Lagrangean relaxation with clusters for point-feature cartographic label placement problems
Computers and Operations Research
A greedy randomized adaptive search procedure for the point-feature cartographic label placement
Computers & Geosciences
A new hybrid heuristic for driver scheduling
International Journal of Hybrid Intelligent Systems - VIII Brazilian Symposium On Neural Networks
A Constructive Genetic Algorithm for permutation flowshop scheduling
Computers and Industrial Engineering
A decomposition approach for the probabilistic maximal covering location-allocation problem
Computers and Operations Research
The capacitated centred clustering problem
Computers and Operations Research
A hybrid column generation approach for the berth allocation problem
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Time complexity estimation and optimisation of the genetic algorithm clustering method
WSEAS Transactions on Mathematics
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
A Lagrangian relaxation approach for a large scale new variant of capacitated clustering problem
Computers and Industrial Engineering
Genetic algorithm for finding cluster hierarchies
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
EDA-USL: unsupervised clustering algorithm based on estimation of distribution algorithm
International Journal of Wireless and Mobile Computing
Population training heuristics
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Computers and Operations Research
Applying bio-inspired techniques to the p-median problem
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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Genetic algorithms (GAs) have recently been accepted as powerful approaches to solving optimization problems. It is also well-accepted that building block construction (schemata formation and conservation) has a positive influence on GA behavior. Schemata are usually indirectly evaluated through a derived structure. We introduce a new approach called the Constructive Genetic Algorithm (CGA), which allows for schemata evaluation and the provision of other new features to the GA. Problems are modeled as bi-objective optimization problems that consider the evaluation of two fitness functions. This double fitness process, called fg-fitness, evaluates schemata and structures in a common basis. Evolution is conducted considering an adaptive rejection threshold that contemplates both objectives and attributes a rank to each individual in population. The population is dynamic in size and composed of schemata and structures. Recombination preserves good schemata, and mutation is applied to structures to get population diversification. The CGA is applied to two clustering problems in graphs. Representation of schemata and structures use a binary digit alphabet and are based on assignment (greedy) heuristics that provide a clearly distinguished representation for the problems. The clustering problems studied are the classical p-median and the capacitated p-median. Good results are shown for problem instances taken from the literature.