Journal ArticleDOI
Self-organizing feature maps and the travelling salesman problem
TLDR
Based on Kohonen's work on self-organizing feature maps, an algorithm for solving the classical Travelling Salesman Problem is derived, given a set of cities defined by their positions in the plane, which iteratively organizes towards a quasi-optimal solution.About:
This article is published in Neural Networks.The article was published on 1988-01-01. It has received 347 citations till now. The article focuses on the topics: Travelling salesman problem & Lin–Kernighan heuristic.read more
Citations
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Journal ArticleDOI
The self-organizing map
TL;DR: The self-organizing map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications, and an algorithm which order responses spatially is reviewed, focusing on best matching cell selection and adaptation of the weight vectors.
Book
Foundations of neural networks, fuzzy systems, and knowledge engineering
TL;DR: This text is the first to combine the study of neural networks and fuzzy systems, their basics and their use, along with symbolic AI methods to build comprehensive artificial intelligence systems.
Proceedings Article
Neural Combinatorial Optimization with Reinforcement Learning
TL;DR: A framework to tackle combinatorial optimization problems using neural networks and reinforcement learning, and Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes.
The Traveling Salesman Problem: A Case Study in Local Optimization
David S. Johnson,Lyle A. McGeoch +1 more
TL;DR: This chapter discusses how various approaches to combinatorial optimization have been adapted to the TSP and evaluates their relative success in this perhaps atypical domain from both a theoretical and an experimental point of view.
Journal ArticleDOI
Metaheuristics: A bibliography
Ibrahim H. Osman,Gilbert Laporte +1 more
TL;DR: This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics that have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas.
References
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Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Book
Self Organization And Associative Memory
TL;DR: The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
Journal ArticleDOI
Neural computation of decisions in optimization problems
John J. Hopfield,David W. Tank +1 more
TL;DR: Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks.
Journal ArticleDOI
An Effective Heuristic Algorithm for the Traveling-Salesman Problem
S. Lin,Brian W. Kernighan +1 more
TL;DR: This paper discusses a highly effective heuristic procedure for generating optimum and near-optimum solutions for the symmetric traveling-salesman problem based on a general approach to heuristics that is believed to have wide applicability in combinatorial optimization problems.
Book
Feature discovery by competitive learning
David E. Rumelhart,David Zipser +1 more
TL;DR: In this paper, competitive learning is applied to parallel networks of neuron-like elements to discover salient, general features which can be used to classify a set of stimulus input patterns, and these feature detectors form the basis of a multilayer system that serves to learn categorizations of stimulus sets which are not linearly separable.