Randomized algorithms
Fitness Landscapes Based on Sorting and Shortest Paths Problems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
Evolutionary algorithms and matroid optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Probabilistic computations: Toward a unified measure of complexity
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
On the size of weights in randomized search heuristics
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Computing single source shortest paths using single-objective fitness
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Improved analysis methods for crossover-based algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Factorizations of Complete Graphs into Spanning Trees with All Possible Maximum Degrees
Combinatorial Algorithms
Edge-based representation beats vertex-based representation in shortest path problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Black-box search by unbiased variation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
More effective crossover operators for the all-pairs shortest path problem
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
Faster black-box algorithms through higher arity operators
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Simple max-min ant systems and the optimization of linear pseudo-boolean functions
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Black-box complexities of combinatorial problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Unbiased black box search algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Too fast unbiased black-box algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Towards a complexity theory of randomized search heuristics: ranking-based black-box complexity
CSR'11 Proceedings of the 6th international conference on Computer science: theory and applications
Tight analysis of the (1+1)-ea for the single source shortest path problem
Evolutionary Computation
Crossover can provably be useful in evolutionary computation
Theoretical Computer Science
| Hi-index | 5.23 |
Black-box complexity, counting the number of queries needed to find the optimum of a problem without having access to an explicit problem description, was introduced by Droste, Jansen, and Wegener [S. Droste, T. Jansen, I. Wegener, Upper and lower bounds for randomized search heuristics in black-box optimization, Theory of Computing Systems 39 (2006) 525-544] to measure the difficulty of solving an optimization problem via generic search heuristics such as evolutionary algorithms, simulated annealing or ant colony optimization. Since then, a number of similar complexity notions were introduced. However, so far these new notions were only analyzed for artificial test problems. In this paper, we move a step forward and analyze the different black-box complexity notions for two classic combinatorial problems, namely the minimum spanning tree and the single-source shortest path problem. Besides proving bounds for their black-box complexities, our work reveals that the choice of how to model the optimization problem has a significant influence on its black-box complexity. In addition, when regarding the unbiased (symmetry-invariant) black-box complexity of combinatorial problems, it is important to choose a meaningful definition of unbiasedness.