Randomized algorithms
Approximation algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Introduction to Algorithms
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Minimum spanning trees made easier via multi-objective optimization
Natural Computing: an international journal
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
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions
IEEE Transactions on Evolutionary Computation
Benefits and drawbacks for the use of epsilon-dominance in evolutionary multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Computing minimum cuts by randomized search heuristics
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Approximating Minimum Multicuts by Evolutionary Multi-objective Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Runtime Analyses for Using Fairness in Evolutionary Multi-Objective Optimization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Analyses of simple hybrid algorithms for the vertex cover problem*
Evolutionary Computation
On the size of weights in randomized search heuristics
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Single- and multi-objective evolutionary algorithms for graph bisectioning
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Additive approximations of pareto-optimal sets by evolutionary multi-objective algorithms
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Exact Solutions to the Traveling Salesperson Problem by a Population-Based Evolutionary Algorithm
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Greedy Local Search and Vertex Cover in Sparse Random Graphs
TAMC '09 Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation
Fixed-parameter evolutionary algorithms and the vertex cover problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolutionary algorithms and dynamic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Analysis of the (1 + 1)-EA for finding approximate solutions to vertex cover problems
IEEE Transactions on Evolutionary Computation
Runtime analysis of an ant colony optimization algorithm for TSP instances
IEEE Transactions on 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
On the effect of populations in evolutionary multi-objective optimisation**
Evolutionary Computation
Illustration of fairness in evolutionary multi-objective optimization
Theoretical Computer Science
The role of selective pressure when solving symmetric functions in polynomial time
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
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
Analysis of an iterated local search algorithm for vertex cover in sparse random graphs
Theoretical Computer Science
An analysis on recombination in multi-objective evolutionary optimization
Artificial Intelligence
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The main aim of randomized search heuristics is to produce good approximations of optimal solutions within a small amount of time. In contrast to numerous experimental results, there are only a few theoretical ones on this subject. We consider the approximation ability of randomized search heuristics for the class of covering problems and compare single-objective and multi-objective models for such problems. For the Vertex-Cover problem, we point out situations where the multi-objective model leads to a fast construction of optimal solutions while in the single-objective case even no good approximation can be achieved within expected polynomial time. Examining the more general Set-Cover problem we show that optimal solutions can be approximated within a factor of log n, where n is the problem dimension, using the multi-objective approach while the approximation quality obtainable by the single-objective approach in expected polynomial time may be arbitrarily bad.