An Evolutionary Algorithm with Stochastic Hill-Climbing for the Edge-Biconnectivity Augmentation Problem

Authors:
Ivana Ljubic;Günther R. Raidl
Affiliations:
-;-
Venue:
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Year:
2001

Citing 6
Cited 3

Efficient algorithms for finding minimum spanning trees in undirected and directed graphs

Combinatorica
Approximation algorithms for graph augmentation

Journal of Algorithms
A uniform framework for approximating weighted connectivity problems

Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Memetic algorithms: a short introduction

New ideas in optimization
Low-Degree Spanning Trees of Small Weight

SIAM Journal on Computing
A Hybrid GA for the Edge-Biconnectivity Augmentation Problem

PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature

A Memetic Algorithm for Vertex-Biconnectivity Augmentation

Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
A Memetic Algorithm for Minimum-Cost Vertex-Biconnectivity Augmentation of Graphs

Journal of Heuristics
An improved society of hill-climbers and its application on batch process scheduling

Proceedings of the 43rd annual Southeast regional conference - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

Augmenting an existing network with additional links to achieve higher robustness and survivability plays an important role in network design. We consider the problem of augmenting a network with links of minimum total cost in order to make it edge-biconnected, i.e. the failure of a single link will never disconnect any two nodes. A new evolutionary algorithm is proposed that works directly on the set of additional links of a candidate solution. Problem-specific initialization, recombination, and mutation operators use a stochastic hill-climbing procedure. With low computational effort, only locally optimal, feasible candidate solutions are produced. Experimental results are significantly better than those of a previous genetic algorithm concerning final solutions' qualities and especially execution times.