Results 11 -
19 of
19
A Genetic Algorithm for Constrained Seismic Horizon Correlation
- Proceedings of the International Conference on Computer Vision Pattern Recognition and Image Processing (CVPRIP 2002
, 2002
"... A new approach towards automating the interpretation of geological structures like horizons or faults in reflection seismic data images is presented. Although automatic horizon tracking across faults to thereby determine geologically valid correlations is an important and time consuming task, it has ..."
Abstract
-
Cited by 2 (1 self)
- Add to MetaCart
(Show Context)
A new approach towards automating the interpretation of geological structures like horizons or faults in reflection seismic data images is presented. Although automatic horizon tracking across faults to thereby determine geologically valid correlations is an important and time consuming task, it has still not been solved satisfactorily. The reason for this is the difficulty involved in locating non-ambiguous local correlation features due to the small amount of local information contained in seismic images. The method described in this paper provides an enhancement against a solely local feature based analysis by imposing additional geological and geometrical constraints to find a geologically valid solution. We model this process as an activity of searching for an optimum combination of the available knowledge by introducing a genetic algorithm. The application of the method to typical seismic data images resulted in the successful matching of all major horizons across several normal faults.
Horizon Correlation Across Faults Guided By Geological Constraints
- Proceedings of SPIE, Vol. #4667, Electronic Imaging 2002
, 2002
"... A new approach towards automating the interpretation of geological structures like horizons or faults in reflection seismic data images is presented. Horizons are strong reflection events which indicate boundaries between rock formations while faults are discrete fractures across which there is meas ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
A new approach towards automating the interpretation of geological structures like horizons or faults in reflection seismic data images is presented. Horizons are strong reflection events which indicate boundaries between rock formations while faults are discrete fractures across which there is measurable displacement of rock layering. Horizon tracking across faults and thereby determining geologically valid correlations is an important but time consuming task although it has still not been automated satisfactorily. The di#culties of matching horizon segments across faults are due to those types of images which contain only a small amount of local information, furthermore partially disturbed by vague or noisy signals. In this paper we describe a model-based approach which reduces these uncertainties by introducing global features based on geological constraints. Two optimisation methods have been examined: an exhaustive search algorithm which reliably delivers the optimal solution presuming correctness of the model and a more practicable strategy; viz, a genetic algorithm. Both methods successfully matched all selected horizons across normal faults in typical seismic data images.
A SELF-ADAPTIVE HYBRID GENETIC ALGORIHM FOR OPTIMAL GROUNDWATER REMEDIATION DESIGN BY
"... Groundwater contamination is the result of multiple human activities, such as agriculture, industrial practices and military operations. The traditional remediation approach is to combine pump-and-treat for plume containment and contaminant capture, with other remediation technology for source contr ..."
Abstract
- Add to MetaCart
Groundwater contamination is the result of multiple human activities, such as agriculture, industrial practices and military operations. The traditional remediation approach is to combine pump-and-treat for plume containment and contaminant capture, with other remediation technology for source control. Pump-and-treat systems are expensive, typically requiring high installation and operation costs. The traditional solution approach is then to proceed by trial-and-error, evaluating different design alternatives and selecting the best one from those evaluated. A large body of research has demonstrated that coupling optimization models with simulation models can aid in identifying effective remediation designs. The simple genetic algorithm (SGA) is a heuristic technique capable of solving these types of problems. Unfortunately, the solution of these complex problems is generally computationally intensive. This research focuses on the development and use of a hybrid genetic algorithm (HGA), a method that combines the use of SGA with local search to solve a groundwater remediation problem. The inclusion of local search helps to speed up the solution process
A Simple Method for Detecting Domino Convergence and
"... Within a genetic algorithm, all genes may not be created equal. This concept is the central idea explored in this paper. A second and equally important idea is that this inequality in gene importance or salience can be detected and identified within a GA. To support these ideas, a technique fo ..."
Abstract
- Add to MetaCart
(Show Context)
Within a genetic algorithm, all genes may not be created equal. This concept is the central idea explored in this paper. A second and equally important idea is that this inequality in gene importance or salience can be detected and identified within a GA. To support these ideas, a technique for directly measuring genetic diversity within a GA population and thereby indirectly measuring gene-specific importance is provided. Diversity graphs are offered as a powerful technique for visualizing measurement results. Our theories, metrics and tools are tested on GAs for two problem classes and four different selection methods.
Genetic Algorithms and . . . MODELING: APPLICATIONS IN MATERIALS SCIENCE AND CHEMISTRY AND ADVANCES IN SCALABILITY
, 2007
"... Effective and efficient multiscale modeling is essential to advance both the science and synthesis in a wide array of fields such as physics, chemistry, materials science, biology, biotechnology and pharmacology. This study investigates the efficacy and potential of using genetic algorithms for mult ..."
Abstract
- Add to MetaCart
(Show Context)
Effective and efficient multiscale modeling is essential to advance both the science and synthesis in a wide array of fields such as physics, chemistry, materials science, biology, biotechnology and pharmacology. This study investigates the efficacy and potential of using genetic algorithms for multiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two non-trivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multi-timescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span orders-of-magnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a non-trivial example of vacancy-assisted migration on a surface of a face-centered cubic (fcc) Copper-Cobalt (CuxCo1−x) alloy show that GP predicts all barriers with 0.1 % error from calculations for less than 3 % of active
Voice Matching Using Genetic Algorithm
"... In this paper, the use of Genetic Algorithm (GA) for voice recognition is described. The practical application of Genetic Algorithm (GA) to the solution of engineering problem is a rapidly emerging approach in the field of control engineering and signal processing. Genetic algorithms are useful for ..."
Abstract
- Add to MetaCart
In this paper, the use of Genetic Algorithm (GA) for voice recognition is described. The practical application of Genetic Algorithm (GA) to the solution of engineering problem is a rapidly emerging approach in the field of control engineering and signal processing. Genetic algorithms are useful for searching a space in multi-directional way from large spaces and poorly defined space. Voice is a signal of infinite information. Digital processing of voice signal is very important for automatic voice recognition technology. Nowadays, voice processing is very much important in security mechanism due to mimicry characteristic. So studying the voice feature extraction in voice processing is very necessary in military, hospital, telephone system, investigation bureau and etc. In order to extract valuable information from the voice signal, make decisions on the process, and obtain results, the data needs to be manipulated and analyzed. In this paper, if the instant voice is not matched with same person’s reference voices in the database, then Genetic Algorithm (GA) is applied between two randomly chosen reference voices. Again the instant voice is compared with the result of Genetic Algorithm (GA) which is used, including its three main steps: selection, crossover and mutation. We illustrate our approach with different sample of voices from human in our institution.
Automatic Fault Identification of a Mechanical System using Genetic Algorithm
"... This paper describes a fault identification technique for mechanical system which is based on genetic algorithm using training set. The real-world application of Genetic Algorithm (GA) to the key of engineering problem becomes a rapidly emerging approach in the field of control engineering and signa ..."
Abstract
- Add to MetaCart
(Show Context)
This paper describes a fault identification technique for mechanical system which is based on genetic algorithm using training set. The real-world application of Genetic Algorithm (GA) to the key of engineering problem becomes a rapidly emerging approach in the field of control engineering and signal processing. Genetic algorithms are convenient for searching a space in multi-directional way from large spaces and poorly defined space. In this paper Genetic Algorithm is used to identify and evaluate the fault cases. Several methods are employed in the state of art in fault identification. Here one class of efficient method are investigated which is based on optimization technique. Here it is shown that Genetic Algorithm can be used to select smaller subset of features from the large set which together form a new set that can be successful for fault identification and classification tasks. The performance of this present proposed method has been verified through two types of fitness function, namely, square function and polynomial function. Finally, fault detection exercises are performed based on the training set to verify the feasibility of this proposed method. Experimental results show that the fault is distinguished with a high precision through this present work.
Distribution Algorithms
"... This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other advanced estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model bu ..."
Abstract
- Add to MetaCart
(Show Context)
This paper describes and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other advanced estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model building, the structure of the probabilistic model is updated once every few iterations (generations), whereas in the remaining iterations only model parameters (conditional and marginal probabilities) are updated. Since the time complexity of updating model parameters is much lower than the time complexity of learning the model structure, sporadic model building decreases the overall time complexity of model building. The paper shows that for boundedly difficult nearly decomposable and hierarchical optimization problems, sporadic model building leads to a significant model-building speedup that decreases the asymptotic time complexity of model building in hBOA by a factor of Θ(n 0.26)toΘ(n 0.5), where n is the problem size. On the other hand, sporadic model building also increases the number of evaluations until convergence; nonetheless, the evaluation slowdown is insignificant compared to the gains in the asymptotic complexity of model building.
