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Genetic Algorithm For Solving The Traveling Salesman Problem Using Neighbor-Based Constructive Crossover Operator

Authors: Akshay Vyas *1, Dashmeet Kaur Chawla 2;

Genetic Algorithm For Solving The Traveling Salesman Problem Using Neighbor-Based Constructive Crossover Operator

Abstract

In this paper, a new crossover operator named Neighbor-based Constructive Crossover (NCX) is evolved for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The proposed crossover operator uses the better edges present in parents’ structure by comparing the neighboring nodes of a node in order to generate off-springs. The efficacy of the proposed crossover operator, NCX is set against two other crossover operators, single point crossover (SPCX) [19] and sequential constructive crossover (SCX) [1] for several standard TSPLIB instances [2]. Empirical results and observations illustrate that the new crossover operator is better than the SPCX and SCX in terms of quality of solutions.

Keywords

Traveling Salesman Problem, NP-complete, Genetic Algorithm, Sequential Constructive Crossover, Neighbor-based Constructive Crossover.

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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