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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 NTRS1972

Comparison of genetic algorithms with conjugate gradient methods

Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.

Bosworth, J. L.↗
NASA NTRS1975

Genetic technology: Promises and problems

Issues concerning the use of genetic technology are discussed. Some areas discussed include treating genetic disease, prenatal diagnosis and selective abortion, screening for genetic disease, and genetic counseling. Policy issues stemming from these capabilities are considered.

Frankel, M. S.↗
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 NTRS1971

GENET note no. 1

The general features of the GENET system for simulating networks are described. A set of features is presented which are desirable for network simulations and which are expected to be achieved by this system. Among these features are: (1) two level network modeling; and (2) problem oriented operations. Several typical network systems are modeled in GENET framework to illustrate various of the features and to show its applicability.

Yeh, J. W.↗
NASA NTRS1972

Algebraic, geometric, and stochastic aspects of genetic operators

Genetic algorithms for function optimization employ genetic operators patterned after those observed in search strategies employed in natural adaptation. Two of these operators, crossover and inversion, are interpreted in terms of their algebraic and geometric properties. Stochastic models of the operators are developed which are employed in Monte Carlo simulations of their behavior.

Foo, N. Y.↗
NASA NTRS1974

Study of genetic direct search algorithms for function optimization

The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.

Zeigler, B. P.↗
NASA NTRS1974

On the possible origin and evolution of the genetic code

The genetic code is examined for indications of possible preceding codes that existed during early evolution. Eight of the 20 amino acids are coded by 'quartets' of codons with fourfold degeneracy, and 16 such quartets can exist, so that an earlier code could have provided for 15 or 16 amino acids, rather than 20. If twofold degeneracy is postulated for the first position of the codon, there could have been ten amino acids in the code. It is speculated that these may have been phenylalanine, valine, proline, alanine, histidine, glutamine, glutanic acid, aspartic acid, cysteine and glycine. There is a notable deficiency of arginine in proteins, despite the fact that it has six codons. Simultaneously, there is more lysine in proteins than would be expected from its two codons, if the four bases in mRNA are equiprobable and are arranged randomly. It is speculated that arginine is an 'intruder' into the genetic code, and that it may have displayed another amino acid such as ornithine, or may even have displayed lysine from some of its previous codon assignments. As a result, natural selection has favored lysine against the fact that it has only two codons.

Jukes, T. H.↗
NASA NTRS1974

Coacervate droplets, proteinoid microspheres, and the genetic apparatus

Differences between typical coacervate droplets and typical proteinoid microspheres are examined. It is pointed out that coacervate droplets are produced from polymers obtained from contemporary organisms. The microspheres considered are aggregates of proteinoid formed from monomeric amino acids under geologically relevant conditions. Aspects regarding the primordial sequence are discussed along with the origin of the genetic apparatus and the genetic code.

Fox, S. W.↗
NASA NTRS1965

The genetic code. II.

Amino acid genetic code in which long sequences in DNA spell out instructions transcribed into RNA AND subsequently into proteins

AMINO ACID↗