On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
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
Are multiple runs of genetic algorithms better than one?
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Runtime analysis of a binary particle swarm optimizer
Theoretical Computer Science
Runtime analysis of the 1-ANT ant colony optimizer
Theoretical Computer Science
Analysis of evolutionary algorithms: from computational complexity analysis to algorithm engineering
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Sharp bounds by probability-generating functions and variable drift
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On the approximation ability of evolutionary optimization with application to minimum set cover
Artificial Intelligence
Artificial immune systems for optimisation
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
A method to derive fixed budget results from expected optimisation times
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Artificial immune systems for optimisation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Randomised search heuristics are used in practice to solve difficult problems where no good problem-specific algorithm is known. They deliver a solution of acceptable quality in reasonable time in many cases. When theoretically analysing the performance of randomised search heuristics one usually considers the average time needed to find an optimal solution or one of a pre-specified approximation quality. This is very different from practice where usually the algorithm is stopped after some time. For a theoretical analysis this corresponds to investigating the quality of the solution obtained after a pre-specified number of function evaluations called budget. Such a perspective is taken here and two simple randomised search heuristics, random local search and the (1+1) evolutionary algorithm, are analysed on simple and well-known example functions. If the budget is significantly smaller than the expected time needed for optimisation the behaviour of the algorithms can be very different depending on the problem at hand. Precise analytical results are proven. They demonstrate novel and interesting challenges in the analysis of randomised search heuristics. The potential of this different perspective to provide a more practically useful theory is shown.