Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
Fitness Landscapes Based on Sorting and Shortest Paths Problems
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
PPSN VII Proceedings of the 7th 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
What's AI Done for Me Lately? Genetic Programming's Human-Competitive Results
IEEE Intelligent Systems
Minimum spanning trees made easier via multi-objective optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Better-Than-Greedy Approximation Algorithm for the Minimum Set Cover Problem
SIAM Journal on Computing
Greedy in approximation algorithms
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Computing minimum cuts by randomized search heuristics
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
Analyses of simple hybrid algorithms for the vertex cover problem*
Evolutionary Computation
Evolutionary algorithms and dynamic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Design innovation for real world applications, using evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Analysis of the (1 + 1)-EA for finding approximate solutions to vertex cover problems
IEEE Transactions on Evolutionary Computation
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
Theory of Randomized Search Heuristics: Foundations and Recent Developments
Theory of Randomized Search Heuristics: Foundations and Recent Developments
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
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Evolutionary algorithms (EAs) are a large family of heuristic optimization algorithms inspired by natural phenomena, and are often used in practice to obtain satisficing instead of optimal solutions. In this work, we investigate a largely underexplored issue: the approximation performance of EAs in terms of how close the obtained solution is to an optimal solution. We study an EA framework named simple EA with isolated population (SEIP) that can be implemented as a single- or multi-objective EA. We present general approximation results of SEIP, and specifically on the minimum set cover problem, we find that SEIP achieves the currently best-achievable approximation ratio. Moreover, on an instance class of the k-set cover problem, we disclose how SEIP can overcome the difficulty that limits the greedy algorithm.