Applications of Evolutionary Computation and Artificial Intelligence in Metallurgical Industry

2019 ◽  
pp. 77-87
Author(s):  
Jianqi An ◽  
Jinhua She ◽  
Huicong Chen ◽  
Min Wu
Keyword(s):  
Artificial Intelligence ◽  
Evolutionary Computation ◽  
Metallurgical Industry

scholarly journals POSSIBILITIES OF APPLICATION MACHINE LEARNING METHODS IN SOLVING ANALYTICAL PROBLEMS IN METALLURGICAL INDUSTRY

2021 ◽  
Vol 9 (3) ◽  
pp. 61-65
Author(s):  
Diana Yusupova ◽  
Sergey Muzalev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Metallurgical Industry ◽  
Learning Opportunities ◽  
High Tech ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Non Linear ◽  
A Company

Background. Machine learning is a promising field for organization in the age of development of high-tech methods of management and organization of the company. As a rule, this term is used in relation to artificial intelligence, namely, machines that could learn independently. Thus, the main goal of this work is to assess the prospects for using these methods for solving various problems in a corporation. Methods. The article introduces the main methods of machine learning, their analysis, linear and non-linear learning methods are given, their use in practice is indicated, and the key advantages of using a trained artificial intelligence in a company are identified. Result. As a result, the author proposes ways of using machine learning methods in a firm, analyzes their advantages and disadvantages, identifies the problems of implementing artificial intelligence learning opportunities in practice.

scholarly journals Modelling other agents through evolutionary behaviours

2021 ◽  
Author(s):  
Yifeng Zeng ◽  
Qiang Ran ◽  
Biyang Ma ◽  
Yinghui Pan
Keyword(s):  
Artificial Intelligence ◽  
Evolutionary Computation ◽  
The Other ◽  
Decision Quality ◽  
Genetic Operators ◽  
New Approach ◽  
Artificial Intelligence Research ◽  
New Methods ◽  
The Subject ◽  
First Time

AbstractModelling other agents is a challenging topic in artificial intelligence research particularly when a subject agent needs to optimise its own decisions by predicting their behaviours under uncertainty. Existing research often leads to a monotonic set of behaviours for other agents so that a subject agent can not cope with unexpected decisions from the other agents. It requires creative ideas about developing diversity of behaviours so as to improve the subject agent’s decision quality. In this paper, we resort to evolutionary computation approaches to generate a new set of behaviours for other agents and solve the complicated agents’ behaviour search and evaluation issues. The new approach starts with the initial behaviours that are ascribed to the other agents and expands the behaviours by using a number of genetic operators in the behaviour evolution. This is the first time that evolutionary techniques are used to modelling other agents in a general multiagent decision framework. We examine the new methods in two well-studied problem domains and provide experimental results in support.

Hybrid System with Artificial Neural Networks and Evolutionary Computation in Civil Engineering

2006 ◽  
pp. 166-187 ◽  
Author(s):  
Juan R. Rabunal ◽  
Juan Puertas
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Computation ◽  
Real Time ◽  
Hybrid System ◽  
Civil Engineering ◽  
Alarm System ◽  
Artificial Neural

This chapter proposes an application of two techniques of artificial intelligence in a civil engineering area: the artificial neural networks (ANN) and the evolutionary computation (EC). In this chapter, it is shown how these two techniques can work together in order to solve a problem in hydrology. This problem consists on modeling the effect of rain on the runoff flow in a typical urban basin. The ultimate goal is to design a real-time alarm system for floods or subsidence warning in various types of urban basins. A case study is included as an example.

Bio-Inspired Algorithms in Bioinformatics II

2011 ◽  
pp. 241-247
Author(s):  
José Antonio Seoane Fernández ◽  
Mónica Miguélez Rico
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Immune System ◽  
Evolutionary Computation ◽  
Computational Models ◽  
Previous Article ◽  
Artificial Intelligence Techniques ◽  
Common Problems ◽  
Bioinspired Algorithms

Our previous article presented several computational models inspired on biological models, such as neural networks, evolutionary computation, swarm intelligence, and the artificial immune system. It also explained the most common problems in bioinformatics to which these models can be applied. The present article presents a series of approaches to bioinformatics tasks that were developed by means of artificial intelligence techniques and focus on bioinspired algorithms such as artificial neural networks and evolutionary computation.

Hybrid System with Artificial Neural Networks and Evolutionary Computation in Civil Engineering

2011 ◽  
pp. 1084-1101
Author(s):  
Juan R. Rabunal ◽  
Jerónimo Puertas
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Computation ◽  
Real Time ◽  
Hybrid System ◽  
Civil Engineering ◽  
Alarm System ◽  
Artificial Neural

This chapter proposes an application of two techniques of artificial intelligence in a civil engineering area: the artificial neural networks (ANN) and the evolutionary computation (EC). In this chapter, it is shown how these two techniques can work together in order to solve a problem in hydrology. This problem consists on modeling the effect of rain on the runoff flow in a typical urban basin. The ultimate goal is to design a real-time alarm system for floods or subsidence warning in various types of urban basins. A case study is included as an example.

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