An Efficient Genetic Algorithm Based Approach for the Minimum Graph Bisection Problem

Summary The goal of the minimum graph bisection problem is to divide the vertices set of the given undirected graph into two equal-size subsets and such that the number of edges connecting vertices in to vertices in is minimized. In this paper, we propose an efficient method based on genetic algorithm with conditional genetic operators. In the proposed approach, the genetic operators are performed conditionally instead of probably to make sure the algorithm has good global search and local search ability; furthermore, a selection method combining roulette selection with tournament selection is presented to reinforce the local search ability. The proposed approach is tested on a large number of instances and is compared with other optimization methods. The experimental results show that the proposed approach is superior to its competitors. V ) , ( E V G = 1 V 2 V 1 V 2 V

[1]  Catherine A. Schevon,et al.  Optimization by simulated annealing: An experimental evaluation , 1984 .

[2]  J. J. Hopfield,et al.  “Neural” computation of decisions in optimization problems , 1985, Biological Cybernetics.

[3]  David S. Johnson,et al.  Some Simplified NP-Complete Graph Problems , 1976, Theor. Comput. Sci..

[4]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[5]  Chih-Chien Tu,et al.  Spectral methods for graph bisection problems , 1998, Comput. Oper. Res..

[6]  Laura A. Sanchis,et al.  Multiple-Way Network Partitioning , 1989, IEEE Trans. Computers.

[7]  Yoshihiro Yamanishi,et al.  A New Neuron Dynamics for Solving the Minimum Graph Bisection Problem , 2007 .

[8]  C. Tovey Hill Climbing with Multiple Local Optima , 1985 .

[9]  Tom V. Mathew Genetic Algorithm , 2022 .

[10]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11]  L. Darrell Whitley,et al.  Test driving three 1995 genetic algorithms: New test functions and geometric matching , 1995, J. Heuristics.

[12]  Brian W. Kernighan,et al.  An efficient heuristic procedure for partitioning graphs , 1970, Bell Syst. Tech. J..

[13]  R. Battiti,et al.  A Seed-Growth Heuristic for Graph Bisection , 2005 .

[14]  Frank Thomson Leighton,et al.  Graph bisection algorithms with good average case behavior , 1984, Comb..

[15]  A. J. Stone,et al.  Logic partitioning , 1966, DAC.

[16]  Lawrence J. Schmitt,et al.  Performance characteristics of alternative genetic algorithmic approaches to the traveling salesman problem using path representation: An empirical study , 1998, Eur. J. Oper. Res..

[17]  Leslie K. Norford,et al.  A design optimization tool based on a genetic algorithm , 2002 .

[18]  Cecilia R. Aragon,et al.  Optimization by Simulated Annealing: An Experimental Evaluation; Part I, Graph Partitioning , 1989, Oper. Res..

[19]  Rong Long Wang,et al.  A genetic algorithm for subset sum problem , 2004, Neurocomputing.