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
Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem
Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem
An Ejection Chain Approach for the Generalized Assignment Problem
INFORMS Journal on Computing
Advanced fitness landscape analysis and the performance of memetic algorithms
Evolutionary Computation - Special issue on magnetic 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
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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The importance of tuning a search algorithm for the specific features of the target search space has been known for quite some time. However, when dealing with multiobjective problems, there are several twists on the conventional notions of fitness landscapes. Multiobjective optimization problems provide additional difficulties for those seeking to study the properties of the search space. However, the requirement of finding multiple candidate solutions to the problem also introduces new potentially exploitable structure. This paper provides a somewhat high-level overview of multiobjective search space and fitness landscape analysis and examines the impact of these features on the multiobjective generalized assignment problem.