scholarly journals Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Biaobiao Zhang ◽  
Yue Wu ◽  
Jiabin Lu ◽  
K.-L. Du
Keyword(s):  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Structural Optimization ◽  
Evolutionary Computation ◽  
Fuzzy Systems ◽  
Parametric Optimization ◽  
Optimization Techniques ◽  
System Modelling ◽  
Discrete Optimization Problem ◽  
Conventional Optimization

Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

SEARCH FOR OPTIMAL FUNCTIONS OF FUZZY SYSTEMS BASED ON BIOINSPIRED EVOLUTIONARY ALGORITHMS

2021 ◽  
Vol 1 ◽  
pp. 55-75
Author(s):  
Alexey V. Kozlov ◽  
◽  
Yuri P. Kondratenko ◽  
Keyword(s):  
Evolutionary Algorithms ◽  
Structural Optimization ◽  
Fuzzy Systems ◽  
Intelligent Systems ◽  
Information Technologies ◽  
Parametric Optimization ◽  
Computer Hardware ◽  
Search Problem ◽  
Membership Functions ◽  
Step Method

Contemporary research in the field of creation and development of intelligent systems based on fuzzy logic is carried out mainly in the direction of developing highly efficient methods for their synthesis and structural-parametric optimization. In recent years, due to the intensive development of information technologies and computer hardware, bioinspired intelligent techniques of global search are quite promising for solving problems of synthesis and optimization of fuzzy systems, which include evolutionary and swarm methods, that simulate the processes of natural selection, as well as collective behavior of various groups of social animals, insects and microorganisms in nature. This paper is devoted to the development and study of a method of optimal membership functions search for fuzzy systems based on bioinspired evolutionary algorithms of global optimization. The obtained method allows finding the optimal membership functions of linguistic terms at solving the compromise problems of multicriteria structural optimization of various fuzzy systems in order to increase their efficiency, as well as to reduce the degree of complexity of further parametric optimization. In the proposed method for finding the global optimum of the problem being solved, the iterative procedures are carried out on the basis of combination of several different bioinspired evolutionary algorithms with subsequent analysis of the results obtained and the choice of the best variant of the membership function vector. The paper outlines the theoretical foundations and information model for the implementation of the computational step-by-step method for structural optimization of fuzzy systems, as well as presents various options for carrying out its search procedures. In particular, the features of the application and adaptation to the search problem to be solved of such bioinspired evolutionary algorithms as genetic, artificial immune systems and biogeographic are discussed.

scholarly journals Evolutionary Algorithms for Constrained Parameter Optimization Problems

1996 ◽  
Vol 4 (1) ◽  
pp. 1-32 ◽  
Author(s):  
Zbigniew Michalewicz ◽  
Marc Schoenauer
Keyword(s):  
Nonlinear Programming ◽  
Evolutionary Algorithms ◽  
Evolutionary Computation ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Test Cases ◽  
Nonlinear Constraints ◽  
Nonlinear Programming Problem ◽  
General Nonlinear Programming

Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.

EVOLUTIONARY OPTIMIZATION (EvOpt): A BRIEF REVIEW AND ANALYSIS

2003 ◽  
Vol 03 (04) ◽  
pp. 311-330 ◽  
Author(s):  
RUHUL SARKER ◽  
JOARDER KAMRUZZAMAN ◽  
CHARLES NEWTON
Keyword(s):  
Evolutionary Algorithms ◽  
Operations Research ◽  
Transportation Problem ◽  
Optimization Techniques ◽  
The Other ◽  
Computational Techniques ◽  
Computational Results ◽  
Conventional Optimization ◽  
Mathematical Foundations ◽  
Real World Problems

Evolutionary Computation (EC) has attracted increasing attention in recent years, as powerful computational techniques, for solving many complex real-world problems. The Operations Research (OR)/Optimization community is divided on the acceptability of these techniques. One group accepts these techniques as potential heuristics for solving complex problems and the other rejects them on the basis of their weak mathematical foundations. In this paper, we discuss the reasons for using EC in optimization. A brief review of Evolutionary Algorithms (EAs) and their applications is provided. We also investigate the use of EAs for solving a two-stage transportation problem by designing a new algorithm. The computational results are analyzed and compared with conventional optimization techniques.

SEARCH OF OPTIMAL MEMBERSHIP FUNCTIONS OF FUZZY SYSTEMS BASED ON BIOINSPIRED EVOLUTIONARY ALGORITHMS. PART II. METHOD IMPLEMENTATION AND STUDY OF ITS EFFICIENCY

2021 ◽  
Vol 2 ◽  
pp. 139-159
Author(s):  
Olexey Kozlov ◽  
◽  
Yuri Kondratenko ◽  
Keyword(s):  
Evolutionary Algorithms ◽  
Mobile Robot ◽  
Fuzzy Systems ◽  
Parametric Optimization ◽  
High Efficiency ◽  
Fuzzy Controller ◽  
Artificial Immune Systems ◽  
Membership Functions ◽  
Linguistic Terms ◽  
Mobile Robot Control

This article is devoted to the efficiency of a method of optimal membership functions search for fuzzy systems based on bioinspired evolutionary algorithms of global optimization. The proposed method allows finding the optimal membership functions of linguistic terms at solving the compromise problem of minimizing the objective function and reducing computational costs in the process of further parametric optimization of fuzzy systems. To study the effectiveness of the considered method in this work, the search of the optimal membership functions is conducted for a fuzzy controller of the control system of a multi-purpose mobile robot designed to move along inclined and vertical ferromagnetic surfaces, with the implementation of this method based on 3 bioinspired evolutionary algorithms: genetic, artificial immune systems, biogeo­graphic. The analysis of the obtained results of computer modeling showed that the usage of the proposed method of search of optimal membership functions gives the opportunity to increase significantly the efficiency of the mobile robot control, as well as to reduce the total number of parameters at further parametric optimization of linguistic terms, which confirms the high efficiency of the developed method.

scholarly journals BIBLIOMETRIC MAPPING OF THE COMPUTATIONAL INTELLIGENCE FIELD

2007 ◽  
Vol 15 (05) ◽  
pp. 625-645 ◽  
Author(s):  
NEES JAN VAN ECK ◽  
LUDO WALTMAN
Keyword(s):  
Neural Networks ◽  
Evolutionary Computation ◽  
Computational Intelligence ◽  
Fuzzy Systems ◽  
Optimization Problems ◽  
Control Problems ◽  
Classification Problems ◽  
Current Structure ◽  
The Neural Networks ◽  
Regression Problems

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 522-532 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Subhranginee Das ◽  
Mohammad Dilsad Ansari
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fine Tuning ◽  
Functional Link ◽  
Price Prediction ◽  
Closing Price ◽  
Teaching Learning Based Optimization ◽  
Artificial Neural ◽  
Teaching Learning

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

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