Journal ArticleDOI
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
TLDR
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.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.read more
Citations
More filters
Journal ArticleDOI
A review on genetic algorithm: past, present, and future
TL;DR: The analysis of recent advances in genetic algorithms is discussed and the well-known algorithms and their implementation are presented with their pros and cons with the aim of facilitating new researchers.
Journal ArticleDOI
Review of Deep Learning Algorithms and Architectures
Ajay Shrestha,Ausif Mahmood +1 more
TL;DR: This paper reviews several optimization methods to improve the accuracy of the training and to reduce training time, and delve into the math behind training algorithms used in recent deep networks.
Journal ArticleDOI
GA: A Package for Genetic Algorithms in R
TL;DR: The R package GA is described, a collection of general purpose functions that provide a flexible set of tools for applying a wide range of genetic algorithm methods, ranging from mathematical functions in one and two dimensions known to be hard to optimize with standard derivative-based methods, to some selected statistical problems which require the optimization of user defined objective functions.
Journal ArticleDOI
Discrete cuckoo search algorithm for the travelling salesman problem
TL;DR: An improved and discrete version of the Cuckoo Search (CS) algorithm is presented to solve the famous traveling salesman problem (TSP), an NP-hard combinatorial optimisation problem.
Genetic Algorithm Performance with Different Selection Strategies in Solving TSP
TL;DR: In this paper, a comparison of GA performance in solving travelling salesman problem (TSP) using different parent selection strategy is presented. And the results reveal that tournament and proportional roulette wheel can be superior to the rank-based Roulette wheel selection for smaller problems only and become susceptible to premature convergence as problem size increases.
References
More filters
Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Book
Genetic Programming: On the Programming of Computers by Means of Natural Selection
TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.