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Genetic algorithms for flowshop scheduling problems
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Multi-Objective Optimization Using Evolutionary Algorithms
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Proceedings of the 1st International Conference on Genetic Algorithms
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Proceedings of the 1st International Conference on Genetic Algorithms
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Proceedings of the 1st International Conference on Genetic Algorithms
Personnel assignment problem with hierarchical ordering constraints
Computers and Industrial Engineering
Multi-objective rule mining using genetic algorithms
Information Sciences: an International Journal - Special issue: Soft computing data mining
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
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A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A weighted sum genetic algorithm to support multiple-partymultiple-objective negotiations
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Path planning on a cuboid using genetic algorithms
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
A fuzzy GARCH model applied to stock market scenario using a genetic algorithm
Expert Systems with Applications: An International Journal
Agent-based simulation of competitive and collaborative mechanisms for mobile service chains
Information Sciences: an International Journal
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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A dynamic programming algorithm for tree-like weighted set packing problem
Information Sciences: an International Journal
A genetic algorithm for maximum-weighted tree matching problem
Applied Soft Computing
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Solving the bi-objective personnel assignment problem using particle swarm optimization
Applied Soft Computing
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The assignment problem is a well-known graph optimization problem defined on weighted-bipartite graphs. The objective of the standard assignment problem is to maximize the summation of the weights of the matched edges of the bipartite graph. In the standard assignment problem, any node in one partition can be matched with any node in the other partition without any restriction. In this paper, variations of the standard assignment problem are defined with matching constraints by introducing structures in the partitions of the bipartite graph, and by defining constraints on these structures. According to the first constraint, the matching between the two partitions should respect the hierarchical-ordering constraints defined by forest and level graph structures produced by using the nodes of the two partitions respectively. In order to define the second constraint, the nodes of the partitions of the bipartite graph are distributed into mutually exclusive sets. The set-restriction constraint enforces the rule that in one of the partitions all the elements of each set should be matched with the elements of a set in the other partition. Even with one of these constraints the assignment problem becomes an NP-hard problem. Therefore, the extended assignment problem with both the hierarchical-ordering and set-restriction constraints becomes an NP-hard multi-objective optimization problem with three conflicting objectives; namely, minimizing the numbers of hierarchical-ordering and set-restriction violations, and maximizing the summation of the weights of the edges of the matching. Genetic algorithms are proven to be very successful for NP-hard multi-objective optimization problems. In this paper, we also propose genetic algorithm solutions for different versions of the assignment problem with multiple objectives based on hierarchical and set constraints, and we empirically show the performance of these solutions.