Online optimization of an engine controller by means of a genetic algorithm using history of search

The Version Space Controlled Genetic Algorithms (VGA) uses the structure of the version space to cache generalizations about the performance history of chromosomes in the genetic algorithm. This cached experience is used to constrain the generation of new members of the genetic algorithms population. The VGA is shown to be a specific instantiation of a more general framework, Autonomous Learning Elements (ALE). The capabilities of the VGA system are demonstrated using the Boole problem suggested by Wilson [Wilson 1987]. The performance of the VGA is compared to that of decision trees and genetic algorithms. The results suggest that the VGA is able to exploit a certain set of symbiotic relationships between its components, so that the resulting system performs better than either component individually.

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GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data

GigaScience ◽

10.1093/gigascience/giaa005 ◽

2020 ◽

Vol 9 (3) ◽

Cited By ~ 1

Author(s):

Ekaterina Noskova ◽

Vladimir Ulyantsev ◽

Klaus-Peter Koepfli ◽

Stephen J O’Brien ◽

Pavel Dobrynin

Keyword(s):

Genetic Algorithm ◽

Allele Frequency ◽

Frequency Spectrum ◽

Demographic History ◽

Model Organisms ◽

Demographic Model ◽

Multiple Populations ◽

Demographic Models ◽

Allele Frequency Spectrum ◽

History Of

Abstract Background The demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum (AFS), the distribution of allele frequencies in populations. The joint AFS is commonly used to reconstruct the demographic history of multiple populations, and several methods based on diffusion approximation (e.g., ∂a∂i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS. Results Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint AFS data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Model Analysis, https://github.com/ctlab/GADMA). Conclusions We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer multiple demographic models at different local optima close to the global one, providing a larger set of possible scenarios to further explore demographic history.

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The use of metaheuristics for feature selection and the derivation of diagnostic rules

10.32469/10355/73832 ◽

2019 ◽

Author(s):

◽

Jordan Stevens

Keyword(s):

Genetic Algorithm ◽

Feature Selection ◽

Alcohol Use Disorder ◽

Learning Context ◽

University Of Missouri ◽

Search Spaces ◽

History Of ◽

The University ◽

New Algorithms

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This dissertation includes four chapters that discuss 1) the history of metaheuristics, 2) the development of a genetic algorithm for feature selection, 3) the development of a genetic algorithm for deriving psychiatric diagnoses and 4) a demonstration of deriving shortened diagnostic rules for alcohol use disorder that optimally agree with the DSM-5. The first chapter offers an overview of novel developments in the metaheuristics literature, along with suggestions for future developments. The second and third chapters of this dissertation 1) propose new algorithms that can handle search spaces that are not accessible by current algorithms and 2) examine each component of the proposed algorithms to identify subordinate heuristics that are essential for the success of the algorithm. The final chapter utilizes information obtained from the previous two chapters to assess the performance of an algorithm for deriving diagnostic rules in a supervised learning context.

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Application of Assisted History Match Approach in a Brown Field Saves Thirty Percent in Project Time

10.2118/208250-ms ◽

2021 ◽

Author(s):

Nurudeen Oluwatosin Yusuf ◽

Lynn Silpngarmlers

Keyword(s):

Genetic Algorithm ◽

Current Model ◽

Critical Factor ◽

High Water ◽

Fine Tuning ◽

History Match ◽

Water Breakthrough ◽

Production History ◽

History Of ◽

Water Cut

Abstract Reservoir-H sequence, comprising of three reservoirs (H1, H2 & H3) is one of the most complex reservoirs in Niger-delta. With a combined well-count in excess of sixty producers and injectors and a production history of more than fifty-five years, the reservoir has had a history of challenging simulation studies with average water-cut matches resulting in new wells having high water breakthrough from onset. In the latest effort, an assisted history match using genetic algorithm was employed. This approach is a two-step approach including an identification of all relevant history match parameters for the three reservoirs, followed by a fine-tuning of pressure and saturation history match using genetic algorithm. This approach enabled the identification of aquifer assumptions (architecture and transmissibility) as a critical factor in successfully matching the wells in these reservoirs. In addition to pressure and saturation matches, infill opportunities were further validated by tracking current reservoir fluid contacts with the model. The current model has significantly improved the overall water-cut match in more than ten wells that historically had water-breakthrough challenges while using principally global history-match parameters. The elimination of many local changes in the current model is expected to improve both the reliability and the shelf of the model. Also, the variance between estimated contacts compared to actual gas-oil and oil-water contacts around infill locations is less than five feet indicating good predictability of the model. In order to save development cost, multiple opportunities identified in these reservoirs are to be targeted with dual strings. Additional savings were realized by reducing the overall simulations studies timeline by four months.

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GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data

10.1101/407734 ◽

2018 ◽

Author(s):

Ekaterina Noskova ◽

Vladimir Ulyantsev ◽

Klaus-Peter Koepfli ◽

Stephen J. O’Brien ◽

Pavel Dobrynin

Keyword(s):

Genetic Algorithm ◽

Allele Frequency ◽

Frequency Spectrum ◽

Demographic History ◽

Demographic Model ◽

Multiple Populations ◽

Demographic Models ◽

Allele Frequency Spectrum ◽

History Of ◽

Spectrum Data

AbstractThe demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum or AFS, the distribution of allele frequencies in populations. The joint allele frequency spectrum is commonly used to reconstruct the demographic history of multiple populations and several methods based on diffusion approximation (e.g., ∂a∂i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS. Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint allele frequency spectrum data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Analysis, https://github.com/ctlab/GADMA). We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer demographic models at different local optima close to the global one, making it is possible to detect more biology corrected model during further research.

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Experimental Investigation of Damage Detection in Beam Using Dynamic Excitation System

Civil Engineering Journal ◽

10.28991/cej-030925 ◽

2017 ◽

Vol 3 (10) ◽

pp. 920 ◽

Cited By ~ 1

Author(s):

Mehdi Kouhdaragh

Keyword(s):

Genetic Algorithm ◽

Damage Detection ◽

Time History ◽

New Method ◽

Mode Decomposition ◽

Fuzzy Genetic Algorithm ◽

Empirical Mode Decomposition Method ◽

History Of ◽

Structural Failures ◽

Fuzzy Genetic

Most structural failures are due to break in consisting materials. These breaks begin with a crack, the extension of which is a serious threat to the behaviour of structure. Thus the methods of distinguishing and showing cracks are the most important subjects being investigated. In this article, a new smart portable mechanical system to detect damage in beam structures via using fuzzy-genetic algorithm is introduced. Acceleration-time history of the three points of beam is obtained. The signals are then decomposed into smaller components using new EMD (Empirical Mode Decomposition) method with every IMF containing a specific range of frequency. The dominant frequencies of the structure are obtained from these IMFs using Short-term Fourier transform. Subsequently, a new method of damage detection in simply supported beams is introduced based on fuzzy-genetic algorithm. The new method is capable of identifying the location and intensity of the damage. This algorithm is developed to detect the location and intensity of the damage along the beam, which can detect the damage location and intensity based on the pattern of beam frequency variations between undamaged and damaged states.

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