A calculus for the random generation of labelled combinatorial structures
Theoretical Computer Science
A random graph model for massive graphs
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Ranking and unranking permutations in linear time
Information Processing Letters
Extending Population-Based Incremental Learning to Continuous Search Spaces
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A Convergence Proof for the Population Based Incremental Learning Algorithm
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Population-Based Continuous Optimization, Probabilistic Modelling and Mean Shift
Evolutionary Computation
Evolution of Networks: From Biological Nets to the Internet and WWW (Physics)
Evolution of Networks: From Biological Nets to the Internet and WWW (Physics)
The Algorithm Design Manual
Detecting critical nodes in sparse graphs
Computers and Operations Research
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Handbook of Applied Algorithms: Solving Scientific, Engineering, and Practical Problems
Handbook of Applied Algorithms: Solving Scientific, Engineering, and Practical Problems
Robust optimization of graph partitioning and critical node detection in analyzing networks
COCOA'10 Proceedings of the 4th international conference on Combinatorial optimization and applications - Volume Part I
Complexity of the critical node problem over trees
Computers and Operations Research
Branch and cut algorithms for detecting critical nodes in undirected graphs
Computational Optimization and Applications
A derandomized approximation algorithm for the critical node detection problem
Computers and Operations Research
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In this paper the problem of critical node detection (CNDP) is approached using population-based incremental learning (an estimation of distribution algorithm) and simulated annealing optimization algorithms using a combinatorial unranking-based problem representation. This representation is space-efficient and alleviates the need for any repair mechanisms. CNDP is a very recently proposed problem that aims to identify a vertex set V'@?V of k0 nodes from a given graph G=(V,E) such that G(V@?V') has minimum pairwise connectivity. Numerous practical applications for this problem exist, including pandemic disease mitigation, computer security and anti-terrorism. In order to test the proposed heuristics 16 benchmark random graph structures are additionally proposed that utilize Erdos-Renyi, Watts-Strogatz, Forest Fire and Barabasi-Albert models. Each of these models presents different network characteristics, yielding variations in problem difficulty. The relative merits of the two proposed approaches are compared and it is found that the population-based incremental learning approach, using a windowed perturbation operator is able to outperform the proposed simulated annealing method.