Introduction to algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
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
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Algorithm Engineering --- An Attempt at a Definition
Efficient Algorithms
Analysis of an asymmetric mutation operator
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
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Crossover speeds up building-block assembly
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Fixed budget computations: a different perspective on run time analysis
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Artificial immune systems for optimisation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Evolutionary algorithms for the detection of structural breaks in time series: extended abstract
Proceedings of the 15th annual conference companion 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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Analyzing the computational complexity of evolutionary algorithms has become an accepted and important branch in evolutionary computation theory. This is usually done by analyzing the (expected) optimization time measured by means of the number of function evaluations and describing its growth as a function of a measure for the size of the search space. Most often asymptotic results describing only the order of growth are derived. This corresponds to classical analysis of (randomized) algorithms in algorithmics. Recently, the emerging field of algorithm engineering has demonstrated that for practical purposes this analysis can be too coarse and more details of the algorithm and its implementation have to be taken into account in order to obtain results that are valid in practice. Using a very recent analysis of a simple evolutionary algorithm as starting point it is shown that the same holds for evolutionary algorithms. Considering this example it is demonstrated that counting function evaluations more precisely can lead to results contradicting actual run times. Motivated by these limitations of computational complexity analysis an algorithm engineering-like approach is presented.