Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Introduction to Algorithms
Theoretical Computer Science - Natural computing
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
IEEE Transactions on Evolutionary Computation
Theoretical analysis of local search in software testing
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Optimizing monotone functions can be difficult
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Mutation rate matters even when optimizing monotonic functions
Evolutionary Computation
Runtime analysis of the (1+1) EA on computing unique input output sequences
Information Sciences: an International Journal
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Evolutionary algorithms are randomized search heuristics that are often described as robust general purpose problem solvers. It is known, however, that the performance of an evolutionary algorithm may be very sensitive to the setting of some of its parameters. A different perspective is to investigate changes in the expected optimization time due to small changes in the fitness landscape. A class of fitness functions where the expected optimization time of the (1+1) evolutionary algorithm is of the same magnitude for almost all of its members is the set of linear fitness functions. Using linear functions as a starting point, a model of a fitness landscape is devised that incorporates important properties of linear functions. Unexpectedly, the expected optimization time of the (1+1) evolutionary algorithm is clearly larger for this fitness model than on linear functions.