Modeling Data: Analogies in Neural Networks, Simulated Annealing and Genetic Algorithms

2002 ◽  
pp. 147-165 ◽  
Author(s):  
Daniela M. Bailer-Jones ◽  
Coryn A. L. Bailer-Jones
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Modeling Data

Computing with Bees: Attacking Complex Transportation Engineering Problems

2003 ◽  
Vol 12 (03) ◽  
pp. 375-394 ◽  
Author(s):  
Panta Lučić ◽  
Dušan Teodorović
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Transportation Engineering ◽  
Engineering Problems ◽  
Potential Applications ◽  
Complex Engineering ◽  
The Traveling Salesman Problem

The Bee System (an artificial bee swarm) is introduced in this paper. The proposed approach is applied to the Traveling Salesman Problem. The obtained results are very promising. The potential applications of the developed Bee System in the field of transportation engineering are discussed. The Bee System represents the new, successful application of emergent techniques based on natural metaphors, such as simulated annealing, genetic algorithms, and neural networks, to the complex engineering and management problems.

Approximation of Missing Values for Classification and Regression

2013 ◽  
Vol 734-737 ◽  
pp. 2938-2944
Author(s):  
Yi Juan Su ◽  
Zhi Zhong ◽  
Wen Jing Li
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Missing Values ◽  
Deep Insight ◽  
Hybrid Genetic Algorithms ◽  
Classification And Regression ◽  
Insight Into ◽  
Data Mining Application

Missing value of dataset has become a challenging issue in data mining. Although there have been significant efforts to handle it, the methods to deal with missing values in neural networks are undeveloped. This article thus proposes an intelligent method to perform classification and regression in case of missing values. In particular, hybrid genetic algorithms and fast simulated annealing are used to predict them, and their results are further compared. The experimental results give rise to a valuable and deep insight into handling missing values in data mining application.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

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