Discrete Applied Mathematics
Randomized algorithms
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Algorithmics for Hard Problems
Algorithmics for Hard Problems
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Upper and Lower Bounds for Randomized Search Heuristics in Black-Box Optimization
Theory of Computing Systems
Probabilistic computations: Toward a unified measure of complexity
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Black-box search by elimination of fitness functions
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Black-box search by unbiased variation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Faster black-box algorithms through higher arity operators
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
Too fast unbiased black-box algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Memory-restricted black-box complexity of OneMax
Information Processing Letters
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
Black-Box complexity: breaking the O(n logn) barrier of leadingones
EA'11 Proceedings of the 10th international conference on Artificial Evolution
Scalability of population-based search heuristics for many-objective optimization
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Noisy optimization complexity under locality assumption
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Lessons from the black-box: fast crossover-based genetic algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Randomized search heuristics are a broadly used class of general-purpose algorithms. Analyzing them via classical methods of theoretical computer science is a growing field. A big step forward would be a useful complexity theory for such algorithms. We enrich the two existing black-box complexity notions due to Wegener and other authors by the restrictions that not actual objective values, but only the relative quality of the previously evaluated solutions may be taken into account by the algorithm. Many randomized search heuristics belong to this class of algorithms. We show that the new ranking-based model gives more realistic complexity estimates for some problems, while for others the low complexities of the previous models still hold.