Probabilistic algorithm for the directed traveling salesman problem
Mathematics of Operations Research
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Solution of large-scale symmetric travelling salesman problems
Mathematical Programming: Series A and B
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Bin Packing with Adaptive Search
Proceedings of the 1st International Conference on Genetic Algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
How Genetic Algorithms Work: A Critical Look at Implicit Parallelism
Proceedings of the 3rd International Conference on Genetic Algorithms
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
Computers and Industrial Engineering
A comparison of problem decomposition techniques for the FAP
Journal of Heuristics
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Experiments with genetic algorithms using permutation operators applied to the traveling salesman problem (TSP) tend to suggest that these algorithms fail in two respects when applied to very large problems: they scale rather poorly as the number of cities n increases, and the solution quality degrades rapidly. We propose an alternative approach for genetic algorithms applied to hard combinatoric search which we call Evolutionary Divide and Conquer (EDAC). This method has potential for any search problem in which knowledge of good solutions for subproblems can be exploited to improve the solution of the problem itself. The idea is to use the genetic algorithm to explore the space of problem subdivisions rather than the space of solutions themselves. We give some preliminary results of this method applied to the geometric TSP.