On Genetic Algorithms and their Applications

Said, Yasmin H.

doi:10.1016/s0169-7161(04)24013-5

Cited by 16 publications

(9 citation statements)

References 12 publications

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“…79 The use of evolutionary and neural-fuzzy techniques in enhancing the dialog in human-computer interaction systems is currently a very important approach in Natural Language Processing. 1,13,88,52 Zuckerman et al have recently presented a numerical mechanism for the interpretation of spoken referring expressions. Their proposal considers multiple alternatives at different interpretation stages (speech, syntax, semantics, and pragmatics) and combines distance-based functions that represent lexical similarity using two approaches, viz multiplicative and additive.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems

Griol

Iglesias

Ledezma

et al. 2016

Int. J. Neur. Syst.

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This paper proposes a statistical framework to develop user-adapted spoken dialog systems. The proposed framework integrates two main models. The first model is used to predict the user's intention during the dialog. The second model uses this prediction and the history of dialog up to the current moment to predict the next system response. This prediction is performed with an ensemble-based classifier trained for each of the tasks considered, so that a better selection of the next system can be attained weighting the outputs of these specialized classifiers. The codification of the information and the definition of data structures to store the data supplied by the user throughout the dialog makes the estimation of the models from the training data and practical domains manageable. We describe our proposal and its application and detailed evaluation in a practical spoken dialog system.

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Section: Related Workmentioning

confidence: 99%

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems

Griol

Iglesias

Ledezma

et al. 2016

Int. J. Neur. Syst.

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“…Calculus-based versus guided random search techniques were tested through comparison of different solvers whereas enumerative algorithms were discarded since "they cannot compete to the robustness race" when compared with the aforementioned techniques mainly due to the characteristics of their search domains (Said 2005).…”

Section: Optimizationmentioning

confidence: 99%

“…Techniques used during optimization to exhaust the search space are classified in three groups: (1) calculus-based techniques, (2) guided random search techniques, and (3) numerative techniques (Filho et al 1994). Calculus-based versus guided random search techniques were tested through comparison of different solvers whereas enumerative algorithms were discarded since "they cannot compete to the robustness race" when compared with the aforementioned techniques mainly due to the characteristics of their search domains (Said 2005).…”

Section: Optimizationmentioning

confidence: 99%

The Use of Stochastic Optimization in Ground Motion Prediction

Segou¹,

Voulgaris²

2013

Earthquake Spectra

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In recent years stochastic optimization has become popular in modeling highly complex natural systems as opposed to traditional approaches, such as regression analysis. This paper tests the efficiency of regression analysis over mathematical optimization by evaluating the performance of stochastic (genetic algorithms, simulated annealing) and deterministic algorithms (nonlinear least squares, pattern search) in solving minimization problems. Qualitative and quantitative comparison of these solvers reveals that the implementation of genetic algorithms with initial population development, using Latin Hypercube sampling, outperformed other algorithms. Using the aforementioned solver, ground motion prediction equations are derived for the estimation of horizontal peak ground acceleration, velocity, and 5% damped spectral acceleration ordinates at discrete period estimators between 0.05 s and 2 s. Ground motion for stiff and soft soil sites is amplified by a constant factor with respect to rock site. These strong motion prediction equations are proposed for application in the range M4.5-M6.6 and for distances up to 150 km.

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“…A search space defined by the problem and an objective function to be minimized (in this case a measure of an error function) in order to guide the search are also important components of the EA. EAs have been applied to very different problems in very different fields of research, in Physics, Chemistry, Computer Science or Civil Engineering problems [28,29,33]. Color quantization is an problem in Computer Science with extensions to image vision or even to robotics.…”

Section: Introductionmentioning

confidence: 99%

An incremental-encoding evolutionary algorithm for color reduction in images

Carro-Calvo

Salcedo‐Sanz

Ortiz-García

et al. 2010

ICA

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Color reduction in images is an important problem in image processing, since it is a pre-processing step in applications such as image segmentation or compression. Different methods have been proposed in the literature, several of them involving nature-inspired algorithms such as neural networks. However, not many works involving evolutionary computation techniques have been applied to this problem. This paper proposes a novel evolutionary algorithm to tackle the color reduction of RGB images. The proposed evolutionary algorithm incorporates a procedure called incremental-encoding, consisting in starting the image quantization with a small number of colors, and including additional colors in a gradual form, until reaching the final number of quantization colors. In the experiments carried out we show that the incremental-encoding evolutionary algorithm improves the performance of the standard evolutionary algorithm in this problem. Also we show that it obtains better results than several existing color reduction techniques for color quantization problems.

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On Genetic Algorithms and their Applications

Cited by 16 publications

References 12 publications

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems

The Use of Stochastic Optimization in Ground Motion Prediction

An incremental-encoding evolutionary algorithm for color reduction in images

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