Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Evolutionary Algorithms: The Role of Mutation and Recombination
Evolutionary Algorithms: The Role of Mutation and Recombination
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Genetic algorithms with multi-parent recombination
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Evolutionary Search for Minimal Elements in Partially Ordered Finite Sets
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Crossover is provably essential for the Ising model on trees
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The one-dimensional Ising model: mutation versus recombination
Theoretical Computer Science
Minimum spanning trees made easier via multi-objective optimization
Natural Computing: an international journal
Approximating covering problems by randomized search heuristics using multi-objective models
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Crossover can provably be useful in evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A new approach to estimating the expected first hitting time of evolutionary algorithms
Artificial Intelligence
Approximating Minimum Multicuts by Evolutionary Multi-objective Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Ignoble Trails - Where Crossover Is Provably Harmful
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Crossover Can Be Constructive When Computing Unique Input Output Sequences
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Additive approximations of pareto-optimal sets by evolutionary multi-objective algorithms
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Improved analysis methods for crossover-based algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Real royal road functions-where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
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
The analysis of a recombinative hill-climber on H-IFF
IEEE Transactions on Evolutionary Computation
Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions
IEEE Transactions on Evolutionary Computation
An analysis on recombination in multi-objective evolutionary optimization
Artificial Intelligence
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Recombination (or called crossover) operators are a kind of characterizing feature of evolutionary algorithms (EAs). The usefulness of recombination operators has been verified empirically in many practical applications, and has also been theoretically studied in single-objective optimization. For multi-objective optimization, however, there lacks strong evidence on whether the recombination operators can lead to a better running time. In this paper, we establish some theoretical support to the use of recombination in multi-objective optimization. We analyze the running time of REMO, a simple multi-objective EA with a recombination operator, on two well-studied bi-objective problems, i.e., the LOTZ and the COCZ problems. Our analysis results disclose that the average running time of REMO on LOTZ and COCZ is Θ(n2) and Θ(n log n), respectively, improved from Θ(n3) and Θ(n2) as when the recombination operator is turned off, respectively. These results imply that the recombination operator is crucial for the efficiency of REMO on these two problems. The analysis also suggests that, generally, recombination operators can be helpful to multi-objective optimization as they may accelerate the filling of the Pareto front through recombining diverse solutions.