Evolutionary Divide and Conquer (I): A Novel Genetic Approach to the TSP

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.

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The Algorithm Animation of Genetic Algorithm of Travelling Salesman Problem

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.2013 ◽

2013 ◽

Vol 411-414 ◽

pp. 2013-2016 ◽

Cited By ~ 1

Author(s):

Guo Zhi Wen

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Traveling Salesman Problem ◽

Travelling Salesman Problem ◽

Traveling Salesman ◽

Travelling Salesman ◽

Algorithm Animation ◽

The Traveling Salesman Problem

The traveling salesman problem is analyzed with genetic algorithms. The best route map and tendency of optimal grade of 500 cities before the first mutation, best route map after 15 times of mutation and tendency of optimal grade of the final mutation are displayed with algorithm animation. The optimal grade is about 0.0455266 for the best route map before the first mutation, but is raised to about 0.058241 for the 15 times of mutation. It shows that through the improvements of algorithms and coding methods, the efficiency to solve the traveling problem can be raised with genetic algorithms.

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Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

Computational Intelligence and Neuroscience ◽

10.1155/2017/7430125 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7 ◽

Cited By ~ 26

Author(s):

Abid Hussain ◽

Yousaf Shad Muhammad ◽

M. Nauman Sajid ◽

Ijaz Hussain ◽

Alaa Mohamd Shoukry ◽

...

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Traveling Salesman Problem ◽

Traveling Salesman ◽

Crossover Operator ◽

Mutation Operators ◽

Path Representation ◽

Hard Problems ◽

The Traveling Salesman Problem ◽

Survival Of The Fittest

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.

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Hybrid Genetics Algorithms for Multiple Sequence Alignment

Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9644-0.ch013 ◽

2016 ◽

pp. 346-366 ◽

Cited By ~ 1

Author(s):

John Tsiligaridis

Keyword(s):

Genetic Algorithm ◽

Tabu Search ◽

Traveling Salesman Problem ◽

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Hybrid Genetic Algorithm ◽

Traveling Salesman ◽

Divide And Conquer ◽

Multiple Sequence ◽

The Traveling Salesman Problem

The purpose of this chapter is to present a set of algorithms and their efficiency for the consistency based Multiple Sequence Alignment (MSA) problem. Based on the strength and adaptability of the Genetic Algorithm (GA) two approaches are developed depending on the MSA type. The first approach, for the non related sequences (no consistency), involves a Hybrid Genetic Algorithm (GA_TS) considering also Tabu Search (TS). The Traveling Salesman Problem (TSP) is also applied determining MSA orders. The second approach, for sequences with consistency, deals with a hybrid GA based on the Divide and Conquer principle (DCP) and it can save space. A consistent dot matrices (CDM) algorithm discovers consistency and creates MSA. The proposed GA (GA_TS_VS) also uses TS but it works with partitions. In conclusion, GAs are stochastic approaches that are proved very beneficial for MSA in terms of their performance.

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