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Results for “applied genetics”

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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Applying a Genetic Algorithm to Reconfigurable Hardware

This paper investigates the feasibility of applying genetic algorithms to solve optimization problems that are implemented entirely in reconfgurable hardware. The paper highlights the pe$ormance/design space trade-offs that must be understood to effectively implement a standard genetic algorithm within a modem Field Programmable Gate Array, FPGA, reconfgurable hardware environment and presents a case-study where this stochastic search technique is applied to standard test-case problems taken from the technical literature. In this research, the targeted FPGA-based platform and high-level design environment was the Starbridge Hypercomputing platform, which incorporates multiple Xilinx Virtex II FPGAs, and the Viva TM graphical hardware description language.

Wells, B. Earl↗
NASA NTRS2004

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Holst, Terry L.↗
NASA NTRS2005

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Holst, Terry L.↗
NASA NTRS1989

Genetic algorithms applied to the scheduling of the Hubble Space Telescope

A prototype system employing a genetic algorithm (GA) has been developed to support the scheduling of the Hubble Space Telescope. A non-standard knowledge structure is used and appropriate genetic operators have been created. Several different crossover styles (random point selection, evolving points, and smart point selection) are tested and the best GA is compared with a neural network (NN) based optimizer. The smart crossover operator produces the best results and the GA system is able to evolve complete schedules using it. The GA is not as time-efficient as the NN system and the NN solutions tend to be better.

Sponsler, Jeffrey L.↗
NASA NTRS1993

A Genetic Algorithm Tool (splicer) for Complex Scheduling Problems and the Space Station Freedom Resupply Problem

The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms.

Wang, Lui↗
NASA NTRS1995

Genetic-Algorithm Tool For Search And Optimization

SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.

Wang, Lui↗
NASA NTRS1998

Automatic Molecular Design using Evolutionary Techniques

Molecular nanotechnology is the precise, three-dimensional control of materials and devices at the atomic scale. An important part of nanotechnology is the design of molecules for specific purposes. This paper describes early results using genetic software techniques to automatically design molecules under the control of a fitness function. The fitness function must be capable of determining which of two arbitrary molecules is better for a specific task. The software begins by generating a population of random molecules. The population is then evolved towards greater fitness by randomly combining parts of the better individuals to create new molecules. These new molecules then replace some of the worst molecules in the population. The unique aspect of our approach is that we apply genetic crossover to molecules represented by graphs, i.e., sets of atoms and the bonds that connect them. We present evidence suggesting that crossover alone, operating on graphs, can evolve any possible molecule given an appropriate fitness function and a population containing both rings and chains. Prior work evolved strings or trees that were subsequently processed to generate molecular graphs. In principle, genetic graph software should be able to evolve other graph representable systems such as circuits, transportation networks, metabolic pathways, computer networks, etc.

Globus, Al↗
NASA NTRS1986

ECUT (Energy Conversion and Utilization Technologies Program). Biocatalysis Project

Presented are the FY 1985 accomplishments, activities, and planned research efforts of the Biocatalysis Project of the U.S. Department of Energy, Energy Conversion and Utilization Technologies (ECUT) Program. The Project's technical activities were organized as follows: In the Molecular Modeling and Applied Genetics work element, research focused on (1) modeling and simulation studies to establish the physiological basis of high temperature tolerance in a selected enzyme and the catalytic mechanisms of three species of another enzyme, and (2) determining the degree of plasmid amplification and stability of several DNA bacterial strains. In the Bioprocess Engineering work element, research focused on (1) studies of plasmid propagation and the generation of models, (2) developing methods for preparing immobilized biocatalyst beads, and (3) developing an enzyme encapsulation method. In the Process Design and Analysis work element, research focused on (1) further refinement of a test case simulation of the economics and energy efficiency of alternative biocatalyzed production processes, (2) developing a candidate bioprocess to determine the potential for reduced energy consumption and facility/operating costs, and (3) a techno-economic assessment of potential advancements in microbial ammonia production.

Source record↗
NASA NTRS1988

ECUT (Energy Conversion and Utilization Technologies) program: Biocatalysis Project

Fiscal year 1987 research activities and accomplishments for the Biocatalysis Project of the U.S. Department of Energy, Energy Conversion and Utilization Technologies (ECUT) Division are presented. The project's technical activities were organized into three work elements. The Molecular Modeling and Applied Genetics work element includes modeling and simulation studies to verify a dynamic model of the enzyme carboxypeptidase; plasmid stabilization by chromosomal integration; growth and stability characteristics of plasmid-containing cells; and determination of optional production parameters for hyper-production of polyphenol oxidase. The Bioprocess Engineering work element supports efforts in novel bioreactor concepts that are likely to lead to substantially higher levels of reactor productivity, product yields, and lower separation energetics. The Bioprocess Design and Assessment work element attempts to develop procedures (via user-friendly computer software) for assessing the economics and energetics of a given biocatalyst process.

Source record↗
NASA NTRS1989

ECUT (Energy Conversion and Utilization Technologies) program: Biocatalysis project

The Annual Report presents the fiscal year (FY) 1988 research activities and accomplishments, for the Biocatalysis Project of the U.S. Department of Energy, Energy Conversion and Utilization Technologies (ECUT) Division. The ECUT Biocatalysis Project is managed by the Jet Propulsion Laboratory, California Institute of Technology. The Biocatalysis Project is a mission-oriented, applied research and exploratory development activity directed toward resolution of the major generic technical barriers that impede the development of biologically catalyzed commercial chemical production. The approach toward achieving project objectives involves an integrated participation of universities, industrial companies and government research laboratories. The Project's technical activities were organized into three work elements: (1) The Molecular Modeling and Applied Genetics work element includes research on modeling of biological systems, developing rigorous methods for the prediction of three-dimensional (tertiary) protein structure from the amino acid sequence (primary structure) for designing new biocatalysis, defining kinetic models of biocatalyst reactivity, and developing genetically engineered solutions to the generic technical barriers that preclude widespread application of biocatalysis. (2) The Bioprocess Engineering work element supports efforts in novel bioreactor concepts that are likely to lead to substantially higher levels of reactor productivity, product yields and lower separation energetics. Results of work within this work element will be used to establish the technical feasibility of critical bioprocess monitoring and control subsystems. (3) The Bioprocess Design and Assessment work element attempts to develop procedures (via user-friendly computer software) for assessing the energy-economics of biocatalyzed chemical production processes, and initiation of technology transfer for advanced bioprocesses.

Baresi, Larry↗
NASA NTRS1990

ECUT: Energy Conversion and Utilization Technologies program. Biocatalysis project

The Biocatalysis Project is a mission-oriented, applied research and exploratory development activity directed toward resolution of the major generic technical barriers that impede the development of biologically catalyzed commercial chemical production. The approach toward achieving project objectives involves an integrated participation of Universities, Industrial Companies and Government Research Laboratories. The Project's technical activities were organized into three work elements: molecular modeling and applied genetics; bioprocess engineering; and bioprocess design and assessment.

Source record↗
NASA NTRS2012

Nemesis Autonomous Test System

A generalized framework has been developed for systems validation that can be applied to both traditional and autonomous systems. The framework consists of an automated test case generation and execution system called Nemesis that rapidly and thoroughly identifies flaws or vulnerabilities within a system. By applying genetic optimization and goal-seeking algorithms on the test equipment side, a "war game" is conducted between a system and its complementary nemesis. The end result of the war games is a collection of scenarios that reveals any undesirable behaviors of the system under test. The software provides a reusable framework to evolve test scenarios using genetic algorithms using an operation model of the system under test. It can automatically generate and execute test cases that reveal flaws in behaviorally complex systems. Genetic algorithms focus the exploration of tests on the set of test cases that most effectively reveals the flaws and vulnerabilities of the system under test. It leverages advances in state- and model-based engineering, which are essential in defining the behavior of autonomous systems. It also uses goal networks to describe test scenarios.

Barltrop, Kevin J.↗
NASA NTRS2003

Genetic models in applied physiology: selected contribution: effects of spaceflight on immunity in the C57BL/6 mouse. I. Immune population distributions

There are several aspects of the spaceflight environment that may lead to changes in immunity: mission-related psychological stress, radiation, and changes in gravity. On December 5, 2001, the space shuttle Endeavor launched for a 12-day mission to examine these effects on C57BL/6 mice for the first time. On their return, assays were performed on the spleen, blood, and bone marrow. In response to flight, there were no significant differences in the general circulating leukocyte proportions. In contrast, there was an increase in splenic lymphocyte percentages, with a corresponding decrease in granulocytes. There was an overall shift in splenic lymphocytes away from T cells toward B cells, and a decrease in the CD4-to-CD8 ratios due to a decrease in T helpers. In contrast, there were proportional increases in bone marrow T cells, with decreases in B cells. Although the blast percentage and count were decreased in flight mice, the CD34(+) population was increased. The data were more consistent with a shift in bone marrow populations rather than a response to changes in the periphery. Many of the results are similar to those using other models. Clearly, spaceflight can influence immune parameters ranging from hematopoiesis to mature leukocyte mechanisms.

Non-NASA Center↗
NASA NTRS2003

Genetic models in applied physiology: selected contribution: effects of spaceflight on immunity in the C57BL/6 mouse. II. Activation, cytokines, erythrocytes, and platelets

This portion of the study quantified the effects of a 12-day space shuttle mission (Space Transport System-108/UF-1) on body and lymphoid organ masses, activation marker expression, cytokine secretion, and erythrocyte and thrombocyte characteristics in C57BL/6 mice. Animals in flight (Flt group) had 10-12% lower body mass compared with ground controls housed either in animal enclosure modules or under standard vivarium conditions (P < 0.001) and the smallest thymus and spleen masses. Percentages of CD25(+) lymphocytes, CD3(+)/CD25(+) T cells, and NK1.1(+)/CD25(+) natural killer cells from Flt mice were higher compared with both controls (P < 0.05). In contrast, CD71 expression was depressed in the Flt and animal enclosure module control mice compared with vivarium control animals (P < 0.001). Secretion of interferon-gamma, IL-2, and IL-4, but not tumor necrosis factor-alpha and IL-5, by splenocytes from Flt mice was decreased relative to either one or both ground controls (P < 0.05). Flt mice also had high red blood cell and thrombocyte counts compared with both sets of controls; low red blood cell volume and distribution width, percentage of reticulocytes, and platelet volume were also noted (P < 0.05) and were consistent with dehydration. These data indicate that relatively short exposure to the spaceflight environment can induce profound changes that may become significant during long-term space missions.

Flight Experiment↗
NASA NTRS1998

New Results in Astrodynamics Using Genetic Algorithms

Generic algorithms have gained popularity as an effective procedure for obtaining solutions to traditionally difficult space mission optimization problems. In this paper, a brief survey of the use of genetic algorithms to solve astrodynamics problems is presented and is followed by new results obtained from applying a Pareto genetic algorithm to the optimization of low-thrust interplanetary spacecraft missions.

generic algorithms optimization problems astrodyna↗
NASA NTRS2001

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Kobayashi, Takahisa↗
NASA NTRS1999

Systems Engineering Design Via Experimental Operation Research: Complex Organizational Metric for Programmatic Risk Environments (COMPRE)

Unique and innovative graph theory, neural network, organizational modeling, and genetic algorithms are applied to the design and evolution of programmatic and organizational architectures. Graph theory representations of programs and organizations increase modeling capabilities and flexibility, while illuminating preferable programmatic/organizational design features. Treating programs and organizations as neural networks results in better system synthesis, and more robust data modeling. Organizational modeling using covariance structures enhances the determination of organizational risk factors. Genetic algorithms improve programmatic evolution characteristics, while shedding light on rulebase requirements for achieving specified technological readiness levels, given budget and schedule resources. This program of research improves the robustness and verifiability of systems synthesis tools, including the Complex Organizational Metric for Programmatic Risk Environments (COMPRE).

Mog, Robert A.↗
NASA NTRS1991

Catalysis and biocatalysis program

The annual report presents the fiscal year (FY) 1990 research activities and accomplishments for the Catalysis and Biocatalysis Program of the Advanced Industrial Concepts Division (AICD), Office of Industrial Technologies of the Department of Energy (DOE). The mission of the AICD is to create a balanced program of high risk, long term, directed interdisciplinary research and development that will improve energy efficiency and enhance fuel flexibility in the industrial sector. The Catalysis and Biocatalysis Program's technical activities were organized into five work elements: the Molecular Modeling and Catalysis by Design element; the Applied Microbiology and Genetics element; the Bioprocess Engineering element; the Separations and Novel Chemical Processes element; and the Process Design and Analysis element.

Source record↗