Fitness Landscapes Based on Sorting and Shortest Paths Problems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Crossover can provably be useful in evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Edge-based representation beats vertex-based representation in shortest path problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Ant colony optimization for stochastic shortest path problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Towards analyzing recombination operators in evolutionary search
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
More effective crossover operators for the all-pairs shortest path problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Black-box complexities of combinatorial problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
How crossover helps in pseudo-boolean optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An analysis on recombination in multi-objective evolutionary optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolutionary algorithms and dynamic programming
Theoretical Computer Science
Running time analysis of Ant Colony Optimization for shortest path problems
Journal of Discrete Algorithms
Reducing the arity in unbiased black-box complexity
Proceedings of the 14th annual conference on Genetic and evolutionary computation
More effective crossover operators for the all-pairs shortest path problem
Theoretical Computer Science
Black-box complexities of combinatorial problems
Theoretical Computer Science
An analysis on recombination in multi-objective evolutionary optimization
Artificial Intelligence
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We deepen the theoretical analysis of the genetic algorithm for the all-pairs shortest path problem proposed by Doerr, Happ and Klein (GECCO 2008). We show that the growth of the paths through crossover operations can be analyzed without the previously used approach of waiting until all paths of a certain length are present in the population. This allows to prove an improved guarantee for the optimization time of O(n3.25 log1/4(n). We also show that this bound is asymptotically tight. Besides the mere run-time result, our analysis is a step towards understanding how crossover works and how it can be analyzed with rigorous methods.