Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
On bicriterion minimal spanning trees: an approximation
Computers and Operations Research
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
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
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
Crossover is provably essential for the Ising model on trees
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Multicriteria Optimization
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
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
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
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
Can quantum search accelerate evolutionary algorithms?
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
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
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
On the approximation ability of evolutionary optimization with application to minimum set cover
Artificial Intelligence
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Edge sets: an effective evolutionary coding of spanning trees
IEEE Transactions on Evolutionary Computation
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
Multiobjective evolutionary algorithms for electric power dispatch problem
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
On algorithm-dependent boundary case identification for problem classes
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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Evolutionary algorithms (EAs) are increasingly popular approaches to multi-objective optimization. One of their significant advantages is that they can directly optimize the Pareto front by evolving a population of solutions, where the recombination (also called crossover) operators are usually employed to reproduce new and potentially better solutions by mixing up solutions in the population. Recombination in multi-objective evolutionary algorithms is, however, mostly applied heuristically. In this paper, we investigate how from a theoretical viewpoint a recombination operator will affect a multi-objective EA. First, we employ artificial benchmark problems: the Weighted LPTNO problem (a generalization of the well-studied LOTZ problem), and the well-studied COCZ problem, for studying the effect of recombination. Our analysis discloses that recombination may accelerate the filling of the Pareto front by recombining diverse solutions and thus help solve multi-objective optimization. Because of this, for these two problems, we find that a multi-objective EA with recombination enabled achieves a better expected running time than any known EAs with recombination disabled. We further examine the effect of recombination on solving the multi-objective minimum spanning tree problem, which is an NP-hard problem. Following our finding on the artificial problems, our analysis shows that recombination also helps accelerate filling the Pareto front and thus helps find approximate solutions faster.