Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Mobile transporter path planning using a genetic algorithm approach

The use of an optimization technique known as a genetic algorithm for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the Space Station which must be able to reach any point of the structure autonomously. Specific elements of the genetic algorithm are explored in both a theoretical and experimental sense. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research. However, trajectory planning problems are common in space systems and the genetic algorithm provides an attractive alternative to the classical techniques used to solve these problems.

Baffes, Paul↗
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 NTRS1991

Systolic array IC for genetic computation

Measuring similarities between large sequences of genetic information is a formidable task requiring enormous amounts of computer time. Geneticists claim that nearly two months of CRAY-2 time are required to run a single comparison of the known database against the new bases that will be found this year, and more than a CRAY-2 year for next year's genetic discoveries, and so on. The DNA IC, designed at HP-ICBD in cooperation with the California Institute of Technology and the Jet Propulsion Laboratory, is being implemented in order to move the task of genetic comparison onto workstations and personal computers, while vastly improving performance. The chip is a systolic (pumped) array comprised of 16 processors, control logic, and global RAM, totaling 400,000 FETS. At 12 MHz, each chip performs 2.7 billion 16 bit operations per second. Using 35 of these chips in series on one PC board (performing nearly 100 billion operations per second), a sequence of 560 bases can be compared against the eventual total genome of 3 billion bases, in minutes--on a personal computer. While the designed purpose of the DNA chip is for genetic research, other disciplines requiring similarity measurements between strings of 7 bit encoded data could make use of this chip as well. Cryptography and speech recognition are two examples. A mix of full custom design and standard cells, in CMOS34, were used to achieve these goals. Innovative test methods were developed to enhance controllability and observability in the array. This paper describes these techniques as well as the chip's functionality. This chip was designed in the 1989-90 timeframe.

Anderson, D.↗
NASA NTRS2000

Modeling the Normal and Neoplastic Cell Cycle with 'Realistic Boolean Genetic Networks': Their Application for Understanding Carcinogenesis and Assessing Therapeutic Strategies

In this paper we show how Boolean genetic networks could be used to address complex problems in cancer biology. First, we describe a general strategy to generate Boolean genetic networks that incorporate all relevant biochemical and physiological parameters and cover all of their regulatory interactions in a deterministic manner. Second, we introduce 'realistic Boolean genetic networks' that produce time series measurements very similar to those detected in actual biological systems. Third, we outline a series of essential questions related to cancer biology and cancer therapy that could be addressed by the use of 'realistic Boolean genetic network' modeling.

Szallasi, Zoltan↗
NASA NTRS2004

A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm

COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions is performed in this study. Also, a response surface approach to robust design is used to develop a new penalty function approach. This new penalty function approach is then compared with the other existing penalty functions.

Ortiz, Francisco↗
NASA NTRS2009

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship s flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm s design, along with mathematical models of the algorithm s performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

Karr, David A.↗
NASA NTRS2007

Scope of Gradient and Genetic Algorithms in Multivariable Function Optimization

Global optimization of a multivariable function - constrained by bounds specified on each variable and also unconstrained - is an important problem with several real world applications. Deterministic methods such as the gradient algorithms as well as the randomized methods such as the genetic algorithms may be employed to solve these problems. In fact, there are optimization problems where a genetic algorithm/an evolutionary approach is preferable at least from the quality (accuracy) of the results point of view. From cost (complexity) point of view, both gradient and genetic approaches are usually polynomial-time; there are no serious differences in this regard, i.e., the computational complexity point of view. However, for certain types of problems, such as those with unacceptably erroneous numerical partial derivatives and those with physically amplified analytical partial derivatives whose numerical evaluation involves undesirable errors and/or is messy, a genetic (stochastic) approach should be a better choice. We have presented here the pros and cons of both the approaches so that the concerned reader/user can decide which approach is most suited for the problem at hand. Also for the function which is known in a tabular form, instead of an analytical form, as is often the case in an experimental environment, we attempt to provide an insight into the approaches focusing our attention toward accuracy. Such an insight will help one to decide which method, out of several available methods, should be employed to obtain the best (least error) output. *

Shaykhian, Gholam Ali↗
NASA NTRS2009

Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution

The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship's flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm's design, along with mathematical models of the algorithm's performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.

Karr, David A.↗
NASA NTRS2017

Skin-Based DNA Repair Phenotype for Cancer Risk from GCR in Genetically Diverse Populations

Predicting cancer risk associated with cosmic radiation remains a mission-critical challenge for NASA radiation health scientists and mission planners. Epidemiological data are lacking and risk methods do not take individual radiation sensitivity into account. In our approach we hypothesize that genetic factors strongly influence risk of cancer from space radiation and that biomarkers reflecting DNA damage and cell death are ideal tools to predict risk and monitor potential health effects post-flight. At this workshop, we will be reporting the work we have done over the first 9 months of this proposal. Skin cells from 15 different strains of mice already characterized for radiation-induced cancer sensitivity (B6C3F; BALB/cByJ, C57BL/6J, CBA/CaJ, C3H/HeMsNrsf), and 10 strains from the DOE collaborative cross-mouse model were expanded from ear biopsy and cultivated until Passage 3. On average, 3 males and 3 females for each strain were expanded and frozen for further characterization at the NSRL beam line during the NSRL16C run for three LET (350 MeV/n Si, 350 MeV/n Ar and 600 MeV/n Fe) and two ion fluences (1 and 3 particles per cell). The mice work has established new metrics for the usage of Radiation Induced Foci as a marker for various aspect of DNA repair deficiencies. In year 2, we propose to continue characterization of the mouse lines with low LET to identify loci specific to high- versus low- LET and establish genetic linkage for the various DNA repair biomarkers. Correlation with cancer risk from each animals strain and gender will also be investigated. On the human side, we will start characterizing the DNA damage response induced ex-vivo in 200 human's blood donors for radiation sensitivity with a tentative 500 donors by the end of this project. All ex-vivo phenotypic data will be correlated to genetic characterization of each individual human donors using SNP arrays characterization as done for mice. Similarly, ex-vivo phenotypic features from mice will be associated to cancer risk, to identify which biomarkers correlate the most with cancer risk. Genetic traits across humans will also be associated to radiation phenotypic features as a function of age and gender.

radiation space biology↗
NASA NTRS2018

Variability in Galactic Cosmic Radiation- Induced DNA Damage Response in Inbred Mice Is Modulated by Genetics

In radiation biology, the ability to predict cancer risk associated with exposure to low doses of high-LET (Linear Energy Transfer) ionizing radiation remains a challenge. Epidemiological methods lack the sensitivity and power to provide detailed risk estimates for cancer and ignore individual sensitivity. We have hypothesized that DNA repair capacity is the primary factor differentiating peoples radiation sensitivity. We previously showed in immortalized human cell lines that characterizing the dose and time dependence of p53-binding protein 1 (53BP1) foci formation in the nucleus following X-rays exposure is sufficient to predict DNA repair response to any other LET in the same cell line. We now tested this hypothesis across a population of mice with different genetic background. Fibroblast cells were extracted and cultivated from 76 individual mice from 15 different strains and exposed to HZE (high (H) atomic number (Z) and energy (E) galactic cosmic ray particles) particles and X-rays. Individual radiation sensitivities were investigated by high throughput measurement of DNA repair kinetics that evaluated 53bp1 foci numbers as a surrogate for DNA double-strand breaks at various times post-irradiation. Instead of just counting foci which can be hard to distinguish for high-LET or high doses, we also took into account the track structure of high-LET particles to compute the remaining number of unrepaired tracks as a function of time post-irradiation. As expected, the percentage of unrepaired track over a 48 hours follow-up period increased with LET. In addition, repair rate was modulated by genetics, with animals from the same strain showing small variance while large rate differences were observed between strains. Radiation strain sensitivity ranking was estimated based on repair rates from exposure to each LET evaluated in this work, and ranking for high-LET correlated better with ranking from high dose of X-ray, not low dose. At the in-vivo level, drops in T-cells and B-cells number measured 24 hours after 0.1 Gy (Gray) X-ray exposure, correlated with slower DNA repair kinetic in fibroblast cells of the same strains of mice. At the genomic level, mouse genome wide association (GWA) analysis identified seven significant genetic loci on chromosomes 2, 3, 7, 10, 11, 13 and 19 with different significance depending on the LET. Interestingly, for the two highest LET, a common locus on Chromosome 10 was identified with high enrichment for DNA repair associated genes.Overall, this work suggests that repair kinetics of primary skin fibroblasts is a good surrogate marker for in-vivo radiation sensitivities in other tissues and that this response is modulated by genetics. Our study also confirms that DNA repair kinetics following high doses of X-ray can be used to predict radiation sensitivity to high-LET.

High-LET Radiation↗
NASA NTRS1999

Interaction of genetic predisposition and environmental factors in the pathogenesis of idiopathic orthostatic intolerance

BACKGROUND: The hemodynamic and autonomic abnormalities in idiopathic orthostatic intolerance (IOI) have been studied extensively. However, the mechanisms underlying these abnormalities are not understood. If genetic predisposition were important in the pathogenesis of IOI, monozygotic twins of patients with IOI should have similar hemodynamic and autonomic abnormalities. METHODS: We studied two patients with IOI and their identical twins. Both siblings in the first twin pair had orthostatic symptoms, significant orthostatic tachycardia, increased plasma norepinephrine levels with standing, and a greater than normal decrease in systolic blood pressure with trimethaphan infusion. RESULTS: Both siblings had a normal response of plasma renin activity to upright posture. In the second twin pair, only one sibling had symptoms of orthostatic intolerance, an orthostatic tachycardia, and raised plasma catecholamines with standing. The affected sibling had inappropriately low plasma renin activity with standing and was 8-fold more sensitive to the pressor effect of phenylephrine than the unaffected sibling. CONCLUSIONS: We conclude that in some patients, IOI seems to be strongly influenced by genetic factors. In others, however, IOI may be mainly caused by nongenetic factors. These findings suggest that IOI is heterogenous, and that both genetic and environmental factors contribute individually or collectively to create the IOI phenotype.

NASA Discipline Regulatory Physiology↗
NASA NTRS1982

Genetic engineering possibilities for CELSS: A bibliography and summary of techniques

A bibliography of the most useful techniques employed in genetic engineering of higher plants, bacteria associated with plants, and plant cell cultures is provided. A resume of state-of-the-art genetic engineering of plants and bacteria is presented. The potential application of plant bacterial genetic engineering to CELSS (Controlled Ecological Life Support System) program and future research needs are discussed.

Johnson, E. J.↗
NASA NTRS1983

Experimental studies related to the origin of the genetic code and the process of protein synthesis - A review

A survey is presented of the literature on the experimental evidence for the genetic code assignments and the chemical reactions involved in the process of protein synthesis. In view of the enormous number of theoretical models that have been advanced to explain the origin of the genetic code, attention is confined to experimental studies. Since genetic coding has significance only within the context of protein synthesis, it is believed that the problem of the origin of the code must be dealt with in terms of the origin of the process of protein synthesis. It is contended that the answers must lie in the nature of the molecules, amino acids and nucleotides, the affinities they might have for one another, and the effect that those affinities must have on the chemical reactions that are related to primitive protein synthesis. The survey establishes that for the bulk of amino acids, there is a direct and significant correlation between the hydrophobicity rank of the amino acids and the hydrophobicity rank of their anticodonic dinucleotides.

Lacey, J. C., Jr.↗
NASA NTRS1990

The mGA1.0: A common LISP implementation of a messy genetic algorithm

Genetic algorithms (GAs) are finding increased application in difficult search, optimization, and machine learning problems in science and engineering. Increasing demands are being placed on algorithm performance, and the remaining challenges of genetic algorithm theory and practice are becoming increasingly unavoidable. Perhaps the most difficult of these challenges is the so-called linkage problem. Messy GAs were created to overcome the linkage problem of simple genetic algorithms by combining variable-length strings, gene expression, messy operators, and a nonhomogeneous phasing of evolutionary processing. Results on a number of difficult deceptive test functions are encouraging with the mGA always finding global optima in a polynomial number of function evaluations. Theoretical and empirical studies are continuing, and a first version of a messy GA is ready for testing by others. A Common LISP implementation called mGA1.0 is documented and related to the basic principles and operators developed by Goldberg et. al. (1989, 1990). Although the code was prepared with care, it is not a general-purpose code, only a research version. Important data structures and global variations are described. Thereafter brief function descriptions are given, and sample input data are presented together with sample program output. A source listing with comments is also included.

Goldberg, David E.↗
NASA NTRS1991

Distributed genetic algorithms for the floorplan design problem

Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.

Cohoon, James P.↗
NASA NTRS1990

Weather prediction using a genetic memory

Kanaerva's sparse distributed memory (SDM) is an associative memory model based on the mathematical properties of high dimensional binary address spaces. Holland's genetic algorithms are a search technique for high dimensional spaces inspired by evolutional processes of DNA. Genetic Memory is a hybrid of the above two systems, in which the memory uses a genetic algorithm to dynamically reconfigure its physical storage locations to reflect correlations between the stored addresses and data. This architecture is designed to maximize the ability of the system to scale-up to handle real world problems.

Rogers, David↗
NASA NTRS1991

Training product unit neural networks with genetic algorithms

The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

Janson, D. J.↗
NASA NTRS1993

The potential of genetic algorithms for conceptual design of rotor systems

The capabilities of genetic algorithms as a non-calculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the genetic algorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of both an existing helicopter and a proposed helicopter design. This provides a comparison with the existing design and also provides insight into the potential of genetic algorithms in design of new rotors.

Crossley, William A.↗