On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Repair and Brood Selection in the Traveling Salesman Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
Crossover gene selection by spatial location
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
All-pairs bottleneck paths for general graphs in truly sub-cubic time
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Crossover can provably be useful in evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Algorithms and Data Structures: The Basic Toolbox
Algorithms and Data Structures: The Basic Toolbox
Approximating Minimum Multicuts by Evolutionary Multi-objective Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Fast algorithms for (max, min)-matrix multiplication and bottleneck shortest paths
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Computing single source shortest paths using single-objective fitness
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
A survey on metaheuristics for stochastic combinatorial optimization
Natural Computing: an international journal
Improved analysis methods for crossover-based algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Fixed-parameter evolutionary algorithms and the vertex cover problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Evolutionary optimization of flavors
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Approximating covering problems by randomized search heuristics using multi-objective models*
Evolutionary Computation
Abstract convex evolutionary search
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
How crossover helps in pseudo-boolean optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolutionary algorithms and dynamic programming
Theoretical Computer Science
Tight analysis of the (1+1)-ea for the single source shortest path problem
Evolutionary Computation
Running time analysis of Ant Colony Optimization for shortest path problems
Journal of Discrete Algorithms
Worst-case and average-case approximations by simple randomized search heuristics
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
Crossover can provably be useful in evolutionary computation
Theoretical Computer Science
IEEE Transactions on Evolutionary Computation
Edge sets: an effective evolutionary coding of spanning trees
IEEE Transactions on Evolutionary Computation
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The all-pairs shortest path problem is the first non-artificial problem for which it was shown that adding crossover can significantly speed up a mutation-only evolutionary algorithm. Recently, the analysis of this algorithm was refined and it was shown to have an expected optimization time (w. r. t. the number of fitness evaluations) of @Q(n^3^.^2^5(logn)^0^.^2^5). In contrast to this simple algorithm, evolutionary algorithms used in practice usually employ refined recombination strategies in order to avoid the creation of infeasible offspring. We study extensions of the basic algorithm by two such concepts which are central in recombination, namely repair mechanisms and parent selection. We show that repairing infeasible offspring leads to an improved expected optimization time of O(n^3^.^2(logn)^0^.^2). As a second part of our study we prove that choosing parents that guarantee feasible offspring results in an optimization time of O(n^3logn). Both results show that already simple adjustments of the recombination operator can asymptotically improve the runtime of evolutionary algorithms.