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Results for “Genetic Algorithm”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Modeling Metallic Halide Local Structures in Salt Melts Using a Genetic Algorithm
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Pursuit of the Ultimate Regression Model for Samarium(III), Europium(III), and LiCl Using Laser-Induced Fluorescence, Design of Experiments, and a Genetic Algorithm for Feature Selection
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Quantum-inspired genetic algorithm for designing planar multilayer photonic structure
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Co-Optimization of Boost Converter Reliability and Volumetric Power Density Using Genetic Algorithm.
Abstract not provided.
AN AUTOMATED MACHINE LEARNING-GENETIC ALGORITHM FRAMEWORK WITH ACTIVE LEARNING FOR DESIGN OPTIMIZATION
The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.
Leveraging Neural Networks and Genetic Algorithms to Refine Electrode Properties in Redox Flow Batteries
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Thermal Optimization of UO2-Mo Fuel Using Sensitivity Analysis and Genetic Algorithms
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Strong circular dichroism in chiral plasmonic metasurfaces optimized by micro-genetic algorithm
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Co-Optimization of Boost Converter Reliability and Volumetric Power Density Using Genetic Algorithm.
Abstract not provided.
Pu EXAFS Fitting Using Genetic Algorithms (Yearly Highlight) [Slides]
Abstract not provided.
Genetic Algorithm Optimization of A Theory-Based Low-Temperature Cyclopentane Oxidation Model.
Abstract not provided.
Predicting Hydrogen Production from Biomass and Plastic Waste Gasification using Machine Learning and Genetic Algorithms
Machine learning (ML) models were developed to predict process various conditions and compositions by creating a model from a database with approximately 1000 entries and 24 independent variables.