Randomized algorithms
Introduction to Algorithms
Solving Bilevel Multi-Objective Optimization Problems Using Evolutionary Algorithms
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Fixed parameter evolutionary algorithms and maximum leaf spanning trees: a matter of mutation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Theory of Randomized Search Heuristics: Foundations and Recent Developments
An evolutionary algorithm with solution archive for the generalized minimum spanning tree problem
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A parameterized runtime analysis of simple evolutionary algorithms for makespan scheduling
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Parameterized Complexity
| Hi-index | 0.00 |
Bi-level optimisation problems have gained increasing interest in the field of combinatorial optimisation in recent years. With this paper, we start the runtime analysis of evolutionary algorithms for bi-level optimisation problems. We examine the NP-hard generalised minimum spanning tree problem and analyse the two approaches presented by Hu and Raidl [7] (2012) in the context of parameterised complexity (with respect to the number of clusters) that distinguish each other by the chosen representation of possible solutions. Our results show that a (1+1) EA working with the spanning nodes representation is not a fixed-parameter evolutionary algorithm for the problem, whereas the global structure representation enables to solve the problem in fixed-parameter time. Furthermore, we present hard instances for each approach and show that the two approaches are highly complementary by proving that they solve each other's hard instances very efficiently.