Genetic algorithm

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  • When we agreed to edit this book for a second edition, we looked forward to a bit of updating and including some of our latest research results. However, the effort grew rapidly beyond our original vision. The use of genetic algorithms (GAs) is a quickly evolving field of research, and there is much new to recommend. Practitioners are constantly dreaming up new ways to improve and use GAs. Therefore this book differs greatly from the first edition.

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  • Automated summarization methods can be defined as “language-independent,” if they are not based on any languagespecific knowledge. Such methods can be used for multilingual summarization defined by Mani (2001) as “processing several languages, with summary in the same language as input.” In this paper, we introduce MUSE, a languageindependent approach for extractive summarization based on the linear optimization of several sentence ranking measures using a genetic algorithm.

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  • (BQ) Present study attempts to model and optimize the complex electrical discharge machining (EDM) process using soft computing techniques. Artificial neural network (ANN) with back propagation algorithm is used to model the process. As the output parameters are conflicting in nature so there is no single combination of cutting parameters, which provides the best machining performance. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process.

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  • This paper proposes a novel approach to the induction of Combinatory Categorial Grammars (CCGs) by their potential affinity with the Genetic Algorithms (GAs). Specifically, CCGs utilize a rich yet compact notation for lexical categories, which combine with relatively few grammatical rules, presumed universal. Thus, the search for a CCG consists in large part in a search for the appropriate categories for the data-set’s lexical items. We present and evaluates a system utilizing a simple GA to successively search and improve on such assignments. ...

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  • Appendix A: TSP solver using genetic algorithm provides about The outline of the GA for TSP, Mutation by 2-opt, Crossover operator, Greedy Subtour Crossover, Survivor Selection, Other parameters and somethings else.

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  • Chapter 7: Genetic Algorithms to Constraint Satisfaction Problems presents about What is a Genetic Algorithm? Components of GA; How does GA work? Constraint Handling in Gas; GA for 8-Queens Problems; GA for Exam Timetabling Problem; Memetic Algorithms.

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  • Population size is an important parameter in Genetic Algorithms (GAs). How population size is reasonable is a matter of concern when designing programs using GAs. Overall, population size is defined as a given parameters and unchanged in evolution processes. This paper presents research results that GAs population size changes affecting the diversity of populations and apply to multiobjective optimization problems, specific the animal feed optimization problem.

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  • In this paper, we propose a method based on genetic algorithms for university course timetabling problems with some modifications and apply it to real-world datasets in Hanoi Open University.

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  • This algorithm involves some modifications of genetic operators and the employment of memory mechanism to enhance its effectiveness. The proposed approach is implemented to generate test data for Simulink models. The obtained results indicated that IGA outperformed the conventional genetic algorithm in terms of the quality of test sets, and the execution time.

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  • The finite element model based on First Shear Displacement Theory to study the mechanical and electrical behaviors of cantilever laminated composite plate bonded piezoelectric patches on surface is presented. A nine-node isoparametric rectangular element with 5 degrees of freedom for the generalized displacements and 2 electrical degrees of freedom at each node is used. Optimization techniques based on genetic algorithm (GAs) are applied in order to maximize the piezoelectric actuator efficiency, improve the structural performance.

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  • This paper surveys results on complexity of the optimal recombination problem (ORP), which consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. In Part II, we consider the computational complexity of ORPs arising in genetic algorithms for problems on permutations.

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  • This paper surveys results on complexity of the optimal recombination problem (ORP), which consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. We consider efficient reductions of the ORPs. Part I presents the basic principles of optimal recombination with a survey of results on Boolean Linear Programming Problems.

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  • The combination of genetic algorithms, smoothing filters and geophysical tomography is used in solving the geophysical inversion problem. This hybrid technique is developed to improve the results obtained by using genetic algorithms only. The application of smoothing filters can improve the performance of GA implementation for solving the geophysical inversion problem. Some test-examples and the obtained comparative results are presented.

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  • This paper presents an original method based on two nested realvalued genetic algorithms (NRGA), which can optimize the parameters of SVM efficiently and speed up the parameter optimization by orders of magnitude compared to the traditional methods which optimize all the parameters simultaneously.

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  • This research proposes a hybrid approach by combining genetic algorithm (GA) and Continuous Ant Colony Optimization (CACO) to find optimum solutions, using a continuous ant colony algorithm as a mutation of genetic algorithm; the performance of the hybrid algorithm is illustrated using three test functions. The results show the efficiency and capabilities of the new hybrid algorithm in finding the optimum solutions.

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  • In this work, a hybrid approach based on the Support Vector machines (SVM) and genetic algorithms (GA) is developed. The SVM are learning machines that can perform binary classification and real value function approximation (regression estimation) tasks. This tool of the artificial intelligence founded on the theory of the statistical learning was selected for its great capacity of training and generalization.

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  • The order in which requirements are implemented in a system affects the value delivered to the final users in the successive releases of the system. This we are developing a tool through which we can reduce the disagreements between the client and the developer based on the interactive genetic algorithm.

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  • In this work, a high performance double layer microwave absorbing structure for X-Ku band was designed and implemented. Genetic-algorithm (GA) was used to find the optimized thickness and type of material for each layer.

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  • This paper presents a review on genetic algorithms based clustering techniques. Clustering is one of the most important tasks of data mining for exploring data sets. It can be used to extract useful and hidden information from the datasets. Clustering techniques have a large area of applications including bioinformatics, web use data analysis and image analysis etc.

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  • Providers of cloud give opportunity to customers to avoid start-up costs and reduce operating cost and access data properly. So to increase security in cloud computing steganography technique is used using genetic algorithm. This will protect stored data in cloud from unauthorized access.

    pdf5p hongnhan878 12-04-2019 19 0   Download



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