Journal Article10.1023/A:1006529012972
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
TL;DR: This paper presents crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with different representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation.
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Abstract: This paper is the result of a literature study carried out by the authors. It is a review of the different attempts made to solve the Travelling Salesman Problem with Genetic Algorithms. We present crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with different representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation. Likewise, we show the experimental results obtained with different standard examples using combination of crossover and mutation operators in relation with path representation.
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Citations
Pencil-beam delivery pattern optimization increases dose rate for stereotactic FLASH proton therapy.
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Multi-Goal Path Optimization for Robotic Systems with Redundancy based on the Traveling Salesman Problem with Neighborhoods
Iacopo Gentilini
- 01 Jan 2012
TL;DR: Three novel approaches to solve the optimization problem of finding an optimal sequence of optimal configurations of TSPN instances with up to 20 convex neighborhoods are presented and a hybrid random-key Genetic Algorithm is developed to address more general problems with a larger number of possibly non-convex neighborhoods and with different types of edge weighting functions.
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Hybrid Cooperation Models for the Tool Switching Problem
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Using metaheuristic algorithms for parameter estimation in generalized Mallows models
Juan A. Aledo,José A. Gámez,David Molina +2 more
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