On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
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
Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Analysis of an asymmetric mutation operator
Evolutionary Computation
Black-box search by unbiased variation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Quasirandom evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Adaptive population models for offspring populations and parallel evolutionary algorithms
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Fitness-levels for non-elitist populations
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Sharp bounds by probability-generating functions and variable drift
Proceedings of the 13th annual conference on Genetic and evolutionary computation
The use of tail inequalities on the probable computational time of randomized search heuristics
Theoretical Computer Science
Runtime analysis of convex evolutionary search
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Crossover speeds up building-block assembly
Proceedings of the 14th annual conference on Genetic and evolutionary computation
The choice of the offspring population size in the (1,λ) EA
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
When do evolutionary algorithms optimize separable functions in parallel?
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Runtime analysis of evolutionary algorithms: basic introduction
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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We present a new method for proving lower bounds in evolutionary computation based on fitness-level arguments and an additional condition on transition probabilities between fitness levels. The method yields exact or near-exact lower bounds for LO, OneMax, and all functions with a unique optimum. All lower bounds hold for every evolutionary algorithm that only uses standard mutation as variation operator.