Combination of Genetic Algorithm with Dynamic Programming for Solving TSP
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Date
2017-07-02
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Université de M'sila
Abstract
This paper presents a combination of Genetic Algorithm (GA) with
Dynamic Programming (DP) to solve the well-known Travelling
Salesman Problem (TSP). In this work, DP is integrated as a GA
operator with a certain probability. In specific, at a given GA
generation, the individuals are subdivided into a number of equal
segments of genes, and the shortest path on each segment is obtained
by applying a DP algorithm. Since the computational complexity of
the DP is O (k22k), it becomes of O(1) when k is small. Experimental
analyses are conducted to investigate the impact and trade-offs among
DP probability, segment size and time processing on the solution
quality and computational effort. In addition, we will implement a
basic GA approach to compare results and show the contribution of
combination of combination approach. Experimental results on
benchmark instances showed that the combined GA-DP algorithm
reduces significantly the computational effort, produces a clearly
improved solution quality and avoids early premature convergence of
GA.
Description
Keywords
Combinatorial Optimization, Dynamic Programming, Evolutionary Computing, Genetic Algorithm, Traveling Salesman Problem.