• Journal of Digital Convergence
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• v.19 no.10
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• pp.403-410
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• 2021

Given an undirected simple graph, k-club is one of the proposed structures to model social groups that exist in various types in Social Network Analysis (SNA). Maximum k-Club Problem (MkCP) is to find a k-club of maximum cardinality in a graph. This paper introduces a Genetic Algorithm called HGA+DROP which can be used to approximate maximum k-club in graphs. Our algorithm modifies the existing k-CLIQUE & DROP algorithm and utilizes Heuristic Genetic Algorithms (HGA) to obtain multiple k-clubs. We experiment on DIMACS graphs for k = 2, 3, 4 and 5 to compare the performance of the proposed algorithm with existing algorithms.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.156-160
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• 2005

We introduce a fully automatic clustering method to classier candidate paralog clusters from a set of protein sequences within one genome. A set of protein sequences is represented as a set of nodes, each represented by the amino acid sequence for a protein with the sequence similarities among them constituting a set of edges in a graph of protein relationships. We use graph-based clustering methods to identify structurally consistent sets of nodes which are strongly connected with each other. Our results are consistent with those from current leading systems such as COG/KOG and KEGG based on manual curation. All the results are viewable at http://www.cs.rutgers.edu/${\sim}$seabee.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.447-456
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• 2007

The ant colony optimization (ACO) is a probabilistic Meta-heuristic algorithm which has been developed in recent years. Originally ACO was used for solving the well-known Traveling Salesperson Problem. More recently, ACO has been used to solve many difficult problems. In this paper, we develop an ant colony optimization method to solve the maximum independent set problem, which is known to be NP-hard. In this paper, we suggest a new method for local information of ACO. Parameters of the ACO algorithm are tuned by evolutionary operations which have been used in forecasting and time series analysis. To show the performance of the ACO algorithm, the set of instances from discrete mathematics and computer science (DIMACS)benchmark graphs are tested, and computational results are compared with a previously developed ACO algorithm and other heuristic algorithms.

• The KIPS Transactions:PartA
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• v.18A no.2
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• pp.69-74
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• 2011

The minimum dominating set problem of a graph G is to find a smallest possible dominating set. The minimum dominating set problem is a well-known NP-complete problem such that it cannot be solved in polynomial time. Heuristic or approximation algorithm, however, will perform well in certain area of application. In this paper, we suggest three different simulated annealing algorithms and experimentally show better efficiency improvement by applying these algorithms to the graph instances developed by DIMACS.