Kaur, D. and Murugappan, M.M. (2008) A Genetic Algorithm Balancing Exploration and Exploitation for the Traveling Salesman Problem. Annual Meeting of the North American Fuzzy Information Processing Society Fuzzy Information Processing Society, Annual Meeting of the North American, 1-6 May 2008.

has been cited by the following article:

• TITLE: A Rule Based Evolutionary Optimization Approach for the Traveling Salesman Problem

  AUTHORS: Wissam M. Alobaidi, David J. Webb, Eric Sandgren

  KEYWORDS: Traveling Salesman, Evolutionary Optimization, Rule Based Search, Heuristic Optimization, Hybrid Genetic Algorithm

  JOURNAL NAME: Intelligent Information Management, Vol.9 No.4, July 14, 2017

  ABSTRACT: The traveling salesman problem has long been regarded as a challenging application for existing optimization methods as well as a benchmark application for the development of new optimization methods. As with many existing algorithms, a traditional genetic algorithm will have limited success with this problem class, particularly as the problem size increases. A rule based genetic algorithm is proposed and demonstrated on sets of traveling salesman problems of increasing size. The solution character as well as the solution efficiency is compared against a simulated annealing technique as well as a standard genetic algorithm. The rule based genetic algorithm is shown to provide superior performance for all problem sizes considered. Furthermore, a post optimal analysis provides insight into which rules were successfully applied during the solution process which allows for rule modification to further enhance performance.