Results 1 - 10
of
14
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceed ..."
Abstract
-
Cited by 90 (10 self)
- Add to MetaCart
s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
Soft Computing: the Convergence of Emerging Reasoning Technologies
- Soft Computing
, 1997
"... The term Soft Computing (SC) represents the combination of emerging problem-solving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to so ..."
Abstract
-
Cited by 68 (11 self)
- Add to MetaCart
The term Soft Computing (SC) represents the combination of emerging problem-solving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to solve complex, real-world problems. After a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolve NNs (topologies or weights) or to tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagation-type algorithms.
Implementation of evolutionary fuzzy systems
- IEEE Transactions on Fuzzy Systems
, 1999
"... Abstract — In this paper, evolutionary fuzzy systems are discussed in which the membership function shapes and types and the fuzzy rule set including the number of rules inside it are evolved using a genetic (evolutionary) algorithm. In addition, the genetic parameters (operators) of the evolutionar ..."
Abstract
-
Cited by 51 (2 self)
- Add to MetaCart
(Show Context)
Abstract — In this paper, evolutionary fuzzy systems are discussed in which the membership function shapes and types and the fuzzy rule set including the number of rules inside it are evolved using a genetic (evolutionary) algorithm. In addition, the genetic parameters (operators) of the evolutionary algorithm are adapted via a fuzzy system. Benefits of the methodology are illustrated in the process of classifying the iris data set. Possible extensions of the methods are summarized. Index Terms—Fuzzy expert systems, genetic algorithm, membership.
Hybrid soft computing systems: industrial and commercial applications
- Proceedings of the IEEE,vol
, 1999
"... Soft computing (SC) is an association of computing methodologies that includes as its principal members fuzzy logic, neurocomputing, evolutionary computing and probabilistic computing. We present a collection of methods and tools that can be used to perform diagnostics, estimation, and control. Thes ..."
Abstract
-
Cited by 5 (1 self)
- Add to MetaCart
(Show Context)
Soft computing (SC) is an association of computing methodologies that includes as its principal members fuzzy logic, neurocomputing, evolutionary computing and probabilistic computing. We present a collection of methods and tools that can be used to perform diagnostics, estimation, and control. These tools are a great match for real-world applications that are characterized by imprecise, uncertain data and incomplete domain knowledge. We outline the advantages of applying SC techniques and in particular the synergy derived from the use of hybrid SC systems. We illustrate some combinations of hybrid SC systems, such as fuzzy logic controllers (FLC’s) tuned by neural networks (NN’s) and evolutionary computing (EC), NN’s tuned by EC or FLC’s, and EC controlled by FLC’s. We discuss three successful real-world examples of SC applications to industrial equipment diagnostics, freight train control, and residential property valuation.
Hybrid Computational Intelligence Schemes in Complex Domains: An Extended Review
- Lecture Notes in Computer Science
, 2002
"... The increased popularity of hybrid intelligent systems in recent times lies to the extensive success of these systems in many real-world complex problems. The main reason for this success seems to be the synergy derived by the computational intelligent components, such as machine learning, fuzzy ..."
Abstract
-
Cited by 4 (1 self)
- Add to MetaCart
(Show Context)
The increased popularity of hybrid intelligent systems in recent times lies to the extensive success of these systems in many real-world complex problems. The main reason for this success seems to be the synergy derived by the computational intelligent components, such as machine learning, fuzzy logic, neural networks and genetic algorithms. Each of these methodologies provides hybrid systems with complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. In this paper, we briefly present most of those computational intelligent combinations focusing in the development of intelligent systems for the handling of problems in real-world applications.
Soft computing techniques for diagnostics and prognostics. Working
"... This paper presents methods and tools which can be used within the framework of diagnostics and prognostics to accommodate imprecision of real systems. We outline the advantages and disadvantages of the different techniques and show how they can be used in a hybrid fashion to complement each other. ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
This paper presents methods and tools which can be used within the framework of diagnostics and prognostics to accommodate imprecision of real systems. We outline the advantages and disadvantages of the different techniques and show how they can be used in a hybrid fashion to complement each other. We conclude the paper with a number of successful real world examples.
Hybrid Soft Computing Systems: Where Are We Going?
, 2000
"... Soft computing is an association of computing methodologies that includes fuzzy logic, neuro-computing, evolutionary computing, and probabilistic computing. After a brief overview of Soft Computing components, we will analyze some of its most synergistic combinations. We will emphasize the developme ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
Soft computing is an association of computing methodologies that includes fuzzy logic, neuro-computing, evolutionary computing, and probabilistic computing. After a brief overview of Soft Computing components, we will analyze some of its most synergistic combinations. We will emphasize the development of smart algorithm-controllers, such as the use of fuzzy logic to control the parameters of evolutionary computing and, conversely, the application of evolutionary algorithms to tune fuzzy controllers. We will focus on three real-world applications of soft computing that leverage the synergism created by hybrid systems.
IMPULSE NOISE REMOVAL FROM MEDICAL IMAGES USING FUZZY GENETIC ALGORITHM
"... Medical images are analyzed for diagnosis of various diseases. But, they are susceptible to impulse noise. Noise removal can be done much more efficiently by a combination of image filters or a composite filter, than by a single image filter. Determining the appropriate filter combination is a diffi ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
Medical images are analyzed for diagnosis of various diseases. But, they are susceptible to impulse noise. Noise removal can be done much more efficiently by a combination of image filters or a composite filter, than by a single image filter. Determining the appropriate filter combination is a difficult task. In this paper, we propose a technique that uses Fuzzy Genetic Algorithm to find the optimal composite filters for removing all types of impulse noise from medical images. Here, a Fuzzy Rule Base is used to adaptively change the crossover probability of the Genetic Algorithm used to determine the optimal composite filters. The results of simulations performed on a set of standard test images for a wide range of noise corruption levels shows that the proposed method outperforms standard procedures for impulse noise removal both visually and in terms of performance measures such as PSNR, IQI and Tenengrad values.
Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA
"... A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as ..."
Abstract
- Add to MetaCart
(Show Context)
A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model.
