Journal of Computer and System Sciences - 26th IEEE Conference on Foundations of Computer Science, October 21-23, 1985
Swarm intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Metric Based Evolutionary Algorithms
Proceedings of the European Conference on Genetic Programming
Ant Colony Optimization
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
On the Choice of the Offspring Population Size in Evolutionary Algorithms
Evolutionary Computation
Runtime Analysis of the (μ+1) EA on Simple Pseudo-Boolean Functions
Evolutionary Computation
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
Theoretical analysis of rank-based mutation: combining exploration and exploitation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Neighborhood graphs and symmetric genetic operators
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
General lower bounds for the running time of evolutionary algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
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
How crossover helps in pseudo-boolean optimization
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
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
Memory-restricted black-box complexity of OneMax
Information Processing Letters
Runtime analysis of convex evolutionary search
Proceedings of the 14th 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
Black-Box complexity: breaking the O(n logn) barrier of leadingones
EA'11 Proceedings of the 10th international conference on Artificial Evolution
Runtime analysis of mutation-based geometric semantic genetic programming on boolean functions
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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The complexity theory for black-box algorithms, introduced by Droste et al. (2006), describes common limits on the efficiency of a broad class of randomised search heuristics. There is an obvious trade-off between the generality of the black-box model and the strength of the bounds that can be proven in such a model. In particular, the original black-box model allows polynomial complexity for certain NP-complete problems and provides for well-known benchmark problems relatively small lower bounds, which are typically not met by popular search heuristics. In this paper, we introduce a more restricted black-box model which we claim captures the working principles of many randomised search heuristics including simulated annealing, evolutionary algorithms, randomised local search and others. The key concept worked out is an unbiased variation operator. Considering this class of algorithms, significantly better lower bounds on the black-box complexity are proved, amongst them an Ω(n log n) bound for functions with unique optimum. Moreover, a simple unimodal function and gap functions are considered. We show that a simple (1+1) EA is able to match the runtime bounds in several cases