A genetic algorithm for the generalised assignment problem
Computers and Operations Research
P-Complete Approximation Problems
Journal of the ACM (JACM)
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
An Ejection Chain Approach for the Generalized Assignment Problem
INFORMS Journal on Computing
Multiobjective fitness landscape analysis and the design of effective memetic algorithms
Multiobjective fitness landscape analysis and the design of effective memetic algorithms
Instance generators and test suites for the multiobjective quadratic assignment problem
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Set-based multiobjective fitness landscapes: a preliminary study
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Pareto local optima of multiobjective NK-landscapes with correlated objectives
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
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This paper proposes the plateau structure imposed by the Pareto dominance relation as a useful determinant of multiobjective metaheuristic performance. In essence, the dominance relation partitions the search space into a set of equivalence classes, and the probabilities, given a specified neighborhood structure, of moving from one class to another are estimated empirically and used to help assess the likely performance of different flavors of multiobjective search algorithms. The utility of this approach is demonstrated on a number of benchmark multiobjective combinatorial optimization problems. In addition, a number of techniques are proposed to allow this method to be used with larger, real-world problems.