Approximation algorithms for graph augmentation
Journal of Algorithms
Biconnectivity approximations and graph carvings
Journal of the ACM (JACM)
Improved approximation algorithms for uniform connectivity problems
Journal of Algorithms
Approximation algorithms for finding highly connected subgraphs
Approximation algorithms for NP-hard problems
A uniform framework for approximating weighted connectivity problems
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Graphs, Networks and Algorithms
Graphs, Networks and Algorithms
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
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In the design of communication networks, robustness against failures in single links or nodes is an important issue. This paper proposes a new approach for the NP-complete edge-biconnectivity augmentation (E2AUG) problem, in which a given graph G0(V, E0) needs to be augmented by the cheapest possible set of edges AUG so that a single edge deletion does not disconnect G0. The new approach is based on a preliminary reduction of the problem and a genetic algorithm (GA) using a binary vector to represent a set of augmenting edges and therefore a candidate solution. Two strategies are proposed to deal with infeasible solutions that do not lead to edge-biconnectivity. In the first, more traditional variant, infeasible solutions are detected and simply discarded. The second method is a hybrid approach that uses an effective heuristic to repair infeasible solutions by adding usually cheap edges to AUG until the graph augmented with AUG becomes edge-biconnected. The two GA-variants are empirically compared to each other and to another iterative heuristic for the E2AUG problem using instances involving up to 1270 edges.