A framework for adaptive sorting
Discrete Applied Mathematics
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
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Theoretical Aspects of Evolutionary Algorithms
ICALP '01 Proceedings of the 28th International Colloquium on Automata, Languages and Programming,
How mutation and selection solve long-path problems in polynomial expected time
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Running time analysis of evolutionary algorithmson a simplified multiobjective knapsack problem
Natural Computing: an international journal
Minimum spanning trees made easier via multi-objective optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Heuristic shortest path algorithms for transportation applications: state of the art
Computers and Operations Research
Maximum cardinality matchings on trees by randomized local search
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Analysis of a multiobjective evolutionary algorithm on the 0-1 knapsack problem
Theoretical Computer Science
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
Algorithmic analysis of a basic evolutionary algorithm for continuous optimization
Theoretical Computer Science
Speeding up evolutionary algorithms through asymmetric mutation operators
Evolutionary Computation
Expected runtimes of evolutionary algorithms for the Eulerian cycle problem
Computers and Operations Research
Evolutionary Computation
Improved analysis methods for crossover-based algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Guiding single-objective optimization using multi-objective methods
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Edge-based representation beats vertex-based representation in shortest path problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
On the effect of populations in evolutionary multi-objective optimisation**
Evolutionary Computation
Black-box complexities of combinatorial problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Speeding up evolutionary algorithms through restricted mutation operators
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
On the approximation ability of evolutionary optimization with application to minimum set cover
Artificial Intelligence
Order preserving clustering over multiple time course experiments
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Running time analysis of a multiobjective evolutionary algorithm on simple and hard problems
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Rigorous runtime analysis of the (1+1) ES: 1/5-rule and ellipsoidal fitness landscapes
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Black-box complexities of combinatorial problems
Theoretical Computer Science
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Runtime analysis of the (1+1) EA on computing unique input output sequences
Information Sciences: an International Journal
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The analysis of evolutionary algorithms is up to now limited to special classes of functions and fitness landscapes. It is not possible to describe those subproblems of NP-hard optimization problems where certain evolutionary algorithms work in polynomial time. Therefore, fitness landscapes based on important computer science problems as sorting and shortest paths problems are investigated here. Although it cannot be expected that evolutionary algorithms outperform the well-known problem specific algorithms on these simple problems, it is interesting to analyze how evolutionary algorithms work on these fitness landscapes which are based on practical problems. The following results are obtained: - Sorting is the maximization of "sortedness" which is measured by one of several well-known measures of presortedness. The different measures of presortedness lead to fitness landscapes of quite different difficulty for EAs. - Shortest paths problems are hard for all types of EA, if they are considered as single-objective optimization problems, while they are easy as multi-objective optimization problems.