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A Novel Document Clustering Algorithm Using Squared Distance Optimization Through Genetic Algorithms  [PDF]
Harish Verma,Eatesh Kandpal,,Bipul Pandey,Joydip Dhar
International Journal on Computer Science and Engineering , 2010,
Abstract: K-Means Algorithm is most widely used algorithms in document clustering. However, it still suffer some shortcomings like random initialization, solution converges to local minima, and empty cluster formation. Genetic algorithm is often used for document clustering because of its global search and optimization ability over heuristic problems. In this paper, search ability of genetic algorithm has exploited with a modification from the general genetic algorithm by not using the random initial population.A new algorithm for populationinitialization is given in this paper and results are compared withk-means algorithm. (Abstract).
Integrated Optimization of Mechanisms with Genetic Algorithms  [PDF]
Jean-Luc Marcelin
Engineering (ENG) , 2010, DOI: 10.4236/eng.2010.26057
Abstract: This paper offers an integrated optimization of mechanisms with genetic algorithm, the principle of which is to use a neural network as a global calculation program and to couple the network with stochastic methods of optimization. In other words, this paper deals with the integrated optimization of mechanisms with genetic algorithms, and, in conclusion, the possible use of neural networks for complex mechanisms or processes.
Efficient Strategies for Optimization with Genetic Algorithms
Hans- Joachim Bungartz,Igor Trajkovski
Sel?uk Journal of Applied Mathematics , 2002,
Abstract: Evolutionary strategies in general and genetic algorithms in particular have turned out to be of increasing relevance for various classes of optimization problems like combinatory problems as a discrete example or shape optimization as a continuous example. In this paper, we present efficient and powerful strategies for genetic algorithms and their application to two classes of optimization problems. Besides algorithmic aspects concerning the genetic essentials, the focus is put on the efficient implementation, both of the sequential and of the parallel versions.
Genetic Algorithms for multimodal optimization: a review  [PDF]
Noe Casas
Computer Science , 2015,
Abstract: In this article we provide a comprehensive review of the different evolutionary algorithm techniques used to address multimodal optimization problems, classifying them according to the nature of their approach. On the one hand there are algorithms that address the issue of the early convergence to a local optimum by differentiating the individuals of the population into groups and limiting their interaction, hence having each group evolve with a high degree of independence. On the other hand other approaches are based on directly addressing the lack of genetic diversity of the population by introducing elements into the evolutionary dynamics that promote new niches of the genotypical space to be explored. Finally, we study multi-objective optimization genetic algorithms, that handle the situations where multiple criteria have to be satisfied with no penalty for any of them. Very rich literature has arised over the years on these topics, and we aim at offering an overview of the most important techniques of each branch of the field.
Particle Swarm Optimization and Genetic Algorithms
Elisa Valentina One?
Journal of Computer Science and Control Systems , 2009,
Abstract: This paper presents two evolutionary computation techniques: particle swarm optimization – part of swarm intelligence and genetic algorithms – part of the evolutionary algorithms. The basic algorithm for each is reviewed, in case of optimization problems in asearch space. It is presented how each evolutionary computation technique works, and the way in which features from one can be included into the other.
Intelligent Controller Design for DC Motor Speed Control based on Fuzzy Logic-Genetic Algorithms Optimization  [PDF]
Boumediene ALLAOUA,Abdellah LAOUFI,Brahim GASBAOUI,Abdelfatah NASRI
Leonardo Journal of Sciences , 2008,
Abstract: In this paper, an intelligent controller of the DC (Direct current) Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became very strong, gives a very good results and possesses good robustness.
On Application of the Local Search and the Genetic Algorithms Techniques to Some Combinatorial Optimization Problems  [PDF]
Anton Bondarenko
Mathematics , 2010,
Abstract: In this paper the approach to solving several combinatorial optimization problems using the local search and the genetic algorithm techniques is proposed. Initially this approach was developed in purpose to overcome some difficulties inhibiting the application of above mentioned techniques to the problems of the Questionnaire Theory. But when the algorithms were developed it became clear that them could be successfully applied also to the Minimum Set Cover, the 0-1-Knapsack and probably to other combinatorial optimization problems.
Genetic Algorithms: Concepts, Design for Optimization of Process Controllers  [cached]
Rahul Malhotra,Narinder Singh,Yaduvir Singh
Computer and Information Science , 2011, DOI: 10.5539/cis.v4n2p39
Abstract: Genetic Algorithm is a search heuristic that mimics the process of evaluation. Genetic Algorithms can be applied to process controllers for their optimization using natural operators. This paper discusses the concept and design procedure of Genetic Algorithm as an optimization tool. Further, this paper explores the well established methodologies of the literature to realize the workability and applicability of genetic algorithms for process control applications. Genetic Algorithms are applied to direct torque control of induction motor drive, speed control of gas turbine, speed control of DC servo motor for the optimization of control parameters in this work. The simulations were carried out in simulink package of MATLAB. The simulation results show better optimization of hybrid genetic algorithm controllers than fuzzy standalone and conventional controllers.
Genetic Algorithms in Application to the Geometry Optimization of Nanoparticles  [PDF]
Naz?m Dugan,?akir Erko?
Algorithms , 2009, DOI: 10.3390/a2010410
Abstract: Applications of genetic algorithms to the global geometry optimization problem of nanoparticles are reviewed. Genetic operations are investigated and importance of phenotype genetic operations, considering the geometry of nanoparticles, are mentioned. Other efficiency improving developments such as floating point representation and local relaxation are described broadly. Parallelization issues are also considered and a recent parallel working single parent Lamarckian genetic algorithm is reviewed with applications on carbon clusters and SiGe core-shell structures.
Genetic Algorithms in Optimization and Computer Aided Design  [PDF]
Reza Farshadnia
Journal of Applied Sciences , 2001,
Abstract: Developments in computational models of evolutionary processes have led to the realization of powerful, robust and general optimization and adaptive systems collectively called evolutionary algorithms. In this paper, we consider one member of this class of algorithms, the genetic algorithm and describe the features and characteristics that are particularly appropriate for application in control systems engineering. The versatility and robust qualities of the algorithm are considered and a number of application areas described. Some prospespective future directions are also identified.
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