Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Introduction to algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
On the Optimization of Unimodal Functions with the (1 + 1) Evolutionary Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
On the Choice of the Mutation Probability for the (1+1) EA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Setting The Mutation Rate: Scope And Limitations Of The 1/L Heuristic
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Comparing global and local mutations on bit strings
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Immunological Computation: Theory and Applications
Immunological Computation: Theory and Applications
Limitations of existing mutation rate heuristics and how a rank GA overcomes them
IEEE Transactions on Evolutionary Computation
On the brittleness of evolutionary algorithms
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Immune inspired somatic contiguous hypermutation for function optimisation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Optimal fixed and adaptive mutation rates for the leadingones problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Optimizing monotone functions can be difficult
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Drift analysis with tail bounds
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Analyzing different variants of immune inspired somatic contiguous hypermutations
Theoretical Computer Science
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
Algorithmica - Special Issue: Theory of Evolutionary Computation
Algorithmica - Special Issue: Theory of Evolutionary Computation
On the analysis of the immune-inspired B-cell algorithm for the vertex cover problem
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Crossover can provably be useful in evolutionary computation
Theoretical Computer Science
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Fitness function distributions over generalized search neighborhoods in the q-ary hypercube
Evolutionary Computation
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Extending previous analyses on function classes like linear functions, we analyze how the simple 1+1 evolutionary algorithm optimizes pseudo-Boolean functions that are strictly monotonic. These functions have the property that whenever only 0-bits are changed to 1, then the objective value strictly increases. Contrary to what one would expect, not all of these functions are easy to optimize. The choice of the constant c in the mutation probability pn=c/n can make a decisive difference. We show that if c iterations. For c=1, we can still prove an upper bound of On3/2. However, for , we present a strictly monotonic function such that the 1+1 EA with overwhelming probability needs iterations to find the optimum. This is the first time that we observe that a constant factor change of the mutation probability changes the runtime by more than a constant factor.