Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment

2021 ◽  
Vol 33 (17) ◽  
pp. 6589-6615
Author(s):  
Josiah Roberts ◽  
Julia R. S. Bursten ◽  
Chad Risko
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Historical Perspective ◽  
Surface Composition ◽  
Composition Structure

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

Machine learning-based acceleration of Genetic Algorithms for Parameter Extraction of highly dimensional MOSFET Compact Models

2021 ◽  
Author(s):  
Gazmend Alia ◽  
Andi Buzo ◽  
Hannes Maier-Flaig ◽  
Klaus-Willi Pieper ◽  
Linus Maurer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Parameter Extraction ◽  
Compact Models

scholarly journals Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator

2020 ◽  
Vol 17 (4) ◽  
pp. 44-60
Author(s):  
Alberto Antonio Agudelo Aguirre ◽  
Ricardo Alfredo Rojas Medina ◽  
Néstor Darío Duque Méndez
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Stock Market ◽  
Moving Average ◽  
Asset Price ◽  
Technical Analysis ◽  
Investment Strategies ◽  
Historical Data Analysis ◽  
Financial Asset Returns ◽  
Technical Indicator

The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.

Genetic algorithms in machine learning

1996 ◽  
Vol 9 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Attilio Giordana ◽  
Filippo Neri
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms

Predicting Takeover Quality in Conditionally Automated Vehicles Using Machine Learning and Genetic Algorithms

2021 ◽  
pp. 84-89
Author(s):  
Emmanuel de Salis ◽  
Quentin Meteier ◽  
Marine Capallera ◽  
Leonardo Angelini ◽  
Andreas Sonderegger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Automated Vehicles

scholarly journals Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2182
Author(s):  
Damilola Ologunagba ◽  
Shyam Kattel
Keyword(s):  
Machine Learning ◽  
Dft Calculations ◽  
Bimetallic Catalysts ◽  
Density Functional ◽  
Surface Segregation ◽  
Surface Composition ◽  
Bulk Composition ◽  
Statistical Machine Learning ◽  
Bimetallic Surfaces ◽  
Computing Platforms

Surface chemical composition of bimetallic catalysts can differ from the bulk composition because of the segregation of the alloy components. Thus, it is very useful to know how the different components are arranged on the surface of catalysts to gain a fundamental understanding of the catalysis occurring on bimetallic surfaces. First-principles density functional theory (DFT) calculations can provide deeper insight into the surface segregation behavior and help understand the surface composition on bimetallic surfaces. However, the DFT calculations are computationally demanding and require large computing platforms. In this regard, statistical/machine learning methods provide a quick and alternative approach to study materials properties. Here, we trained previously reported surface segregation energies on low index surfaces of bimetallic catalysts using various linear and non-linear statistical methods to find a correlation between surface segregation energies and elemental properties. The results revealed that the surface segregation energies on low index bimetallic surfaces can be predicted using fundamental elemental properties.

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