Abstract—This paper presents a novel genetic algorithm approach for network design with a robust fitness function which finds the best least distance network for any number of nodes. A network design problem for this paper falls under the network topology category which is a minimum spanning tree. Since many researchers have tried to solve this problem for small to mid size, we have explored the use of genetic algorithm with modification but without changing the nature of genetic algorithm. A strong fitness function is developed here for solving this network optimization problem which not only reduces the number of generation rather produces the best result and follow the concept of “Survival of the fittest”. Fitness function is the backbone of the concept of genetic algorithm which directly affects the performance; so one of the main focus of this paper is fitness function. Since this is NP problem and traditional heuristics have had only limited success in solving small to mid size problems, in this paper we have tried to show that genetic algorithm is an alternative solution for this NP problem where conventional deterministic methods are not able to provide the optimal solution.
Index Terms—Genetic Algorithm, Network design, Minimum spanning tree.
Anand kumar is with AMC Engineering College, Bangalore INDIA (email : kumaranandkumar@gmail.com).
Dr N.N. Jani is with Kadi Sarva Vishwa yidyalya, Gandhinagar INDIA. (e-mail: drnnjanicsd@gmail.com).
[PDF]
Cite: Anand Kumar and Dr. N. N. Jani, "A Novel Genetic Algorithm approach for Network Design with Robust Fitness Function,"
International Journal of Computer Theory and Engineering vol. 2, no. 3, pp. 459-465, 2010.
