Fitness Landscapes Based on Sorting and Shortest Paths Problems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
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
Probabilistic computations: Toward a unified measure of complexity
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Crossover can provably be useful in evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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
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
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
Too fast unbiased black-box algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Reducing the arity in unbiased black-box complexity
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Black-box complexities of combinatorial problems
Theoretical Computer Science
Lessons from the black-box: fast crossover-based genetic algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Black-box complexity: from complexity theory to playing mastermind
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Black-box complexity is a complexity theoretic measure for how difficult a problem is to be optimized by a general purpose optimization algorithm. It is thus one of the few means trying to understand which problems are tractable for genetic algorithms and other randomized search heuristics. Most previous work on black-box complexity is on artificial test functions. In this paper, we move a step forward and give a detailed analysis for the two combinatorial problems minimum spanning tree and single-source shortest paths. Besides giving interesting bounds for their black-box complexities, our work reveals that the choice of how to model the optimization problem is non-trivial here. This in particular comes true where the search space does not consist of bit strings and where a reasonable definition of unbiasedness has to be agreed on.