Genetic search: Analysis using fitness moments

Genetic Algorithms are efficient and robust search methods that are being employed in a plethora of applications with extremely large search spaces. The directed search mechanism employed in Genetic Algorithms performs a simultaneous and balanced, exploration of new regions in the search space and exploitation of already discovered regions.This paper introduces the notion of fitness moments for analyzing the working of Genetic Algorithms (GAs). We show that the fitness moments in any generation may be predicted from those of the initial population. Since a knowledge of the fitness moments allows us to estimate the fitness distribution of strings, this approach provides for a method of characterizing the dynamics of GAs. In particular the average fitness and fitness variance of the population in any generation may be predicted. We introduce the technique of fitness-based disruption of solutions for improving the performance of GAs. Using fitness moments, we demonstrate the advantages of using fitness-based disruption. We also present experimental results comparing the performance of a standard GA and GAs (CDGA and AGA) that incorporate the principle of fitness-based disruption. The experimental evidence clearly demonstrates the power of fitness based disruption.

On generating optimal signal probabilities for random tests: A genetic approach

Genetic Algorithms are robust search and optimization techniques. A Genetic Algorithm based approach for determining the optimal input distributions for generating random test vectors is proposed in the paper. A cost function based on the COP testability measure for determining the efficacy of the input distributions is discussed, A brief overview of Genetic Algorithms (GAs) and the specific details of our implementation are described. Experimental results based on ISCAS-85 benchmark circuits are presented. The performance pf our GA-based approach is compared with previous results. While the GA generates more efficient input distributions than the previous methods which are based on gradient descent search, the overheads of the GA in computing the input distributions are larger. To account for the relatively quick convergence of the gradient descent methods, we analyze the landscape of the COP-based cost function. We prove that the cost function is unimodal in the search space. This feature makes the cost function amenable to optimization by gradient-descent techniques as compared to random search methods such as Genetic Algorithms.

Sibling rivalry between seeds within a fruit: Some population genetic models

Competition between seeds within a fruit for parental resources is described using one-locus-two-allele models. While a �normal� allele leads to an equitable distribution of resources between seeds (a situation which also corresponds to the parental optimum), the �selfish� allele is assumed to cause the seed carrying it to usurp a higher proportion of the resources. The outcome of competition between �selfish� alleles is also assumed to lead to an asymmetric distribution of resources, the �winner� being chosen randomly. Conditions for the spread of an initially rare selfish allele and the optimal resource allocation corresponding to the evolutionarily stable strategy, derived for species with n-seeded fruits, are in accordance with expectations based on Hamilton�s inclusive fitness criteria. Competition between seeds is seen to be most intense when there are only two seeds, and decreases with increasing number of seeds, suggesting that two-seeded fruits would be rarer than one-seeded or many-seeded ones. Available data from a large number of plant species are consistent with this prediction of the model.

Variational principles in evolution

For a one-locus selection model, Svirezhev introduced an integral variational principle by defining a Lagrangian which remained stationary on the trajectory followed by the population undergoing selection. It is shown here (i) that this principle can be extended to multiple loci in some simple cases and (ii) that the Lagrangian is defined by a straightforward generalization of the one-locus case, but (iii) that in two-locus or more general models there is no straightforward extension of this principle if linkage and epistasis are present. The population trajectories can be constructed as trajectories of steepest ascent in a Riemannian metric space. A general method is formulated to find the metric tensor and the surface-in the metric space on which the trajectories, which characterize the variations in the gene structure of the population, lie. The local optimality principle holds good in such a space. In the special case when all possible linkage disequilibria are zero, the phase point of the n-locus genetic system moves on the surface of the product space of n higher dimensional unit spheres in a certain Riemannian metric space of gene frequencies so that the rate of change of mean fitness is maximum along the trajectory. In the two-locus case the corresponding surface is a hyper-torus.

Interannual variations of Indian summer monsoon in a GCM: External conditions versus internal feedbacks

The potential predictability of the Indian summer monsoon due to slowly varying sea surface temperature (SST) forcing is examined. Factors responsible for limiting the predictability are also investigated. Three multiyear simulations with the R30 version of the Geophysical Fluid Dynamics Laboratory's climate model are carried out for this purpose, The mean monsoon simulated by this model is realistic including the mean summer precipitation over the Indian continent. The interannual variability of the large-scale component of the monsoon such as the "monsoon shear index" and its teleconnection with Pacific SST is well simulated by the model in a 15-yr integration with observed SST as boundary condition. On regional scales, the skill in simulating the interannual variability of precipitation over the Indian continent by the model is rather modest and its simultaneous correlation with eastern Pacific SST is negative but poor as observed. The poor predictability of precipitation over the Indian region in the model is related to the fact that contribution to the interannual variability over this region due to slow SST variations [El Nino-Southern Oscillation (ENSO) related] is comparable to those due to regional-scale fluctuations unrelated to ENSO SST. The physical mechanism through which ENSO SST tend to produce reduction in precipitation over the Indian continent is also elucidated. A measure of internal variability of the model summer monsoon is obtained from a 20-yr integration of the same model with fixed annual cycle SST as boundary conditions but with predicted soil moisture and snow cover. A comparison of summer monsoon indexes between this run and the observed SST run shows that the internal oscillations can account for a large fraction of the simulated monsoon variability. The regional-scale oscillations in the observed SST run seems to arise from these internal oscillations. It is discovered that most of the interannual internal variability is due to an internal quasi-biennial oscillation (QBO) of the model atmosphere. Such a QBO is also found in the author's third 18-yr simulation in which fixed annual cycle of SST as well as soil moisture and snow cover are prescribed. This shows that the model QBO is not due to land-surface-atmosphere interaction. It is proposed that the model QBO arises due to an interaction between nonlinear intraseasonal oscillations and the annual cycle. Spatial structure of the QBO and its role in limiting the predictability of the Indian summer monsoon is discussed.

Variations in the Levels of Aminoacylation and Modified Nucleotide Content Between Total Transfer-RNAS From Chloroplasts and Etioplasts in Cucumber Cotyledons

Total tRNAs isolated from chloroplasts and etioplasts of cucumber cotyledons were compared with respect to amino acid acceptance, isoacceptor distribution and extent of modification. Aminoacylation of the tRNAs with nine different amino acids studied indicated that the relative acceptor activities of chloroplast total tRNAs for four amino acids are significantly higher than etioplast total tRNAs. Two dimensional polyacrylamide gel electrophoresis (2D-PAGE) of chloroplast total tRNAs separated at least 32 spots, while approximately 41 spots were resolved from etioplast total tRNAs. Comparison of the reversed-phase chromatography (RPC-5) profiles of chloroplast and etioplast leucyl-, lysyl-, phenylalanyl-, and valyl-tRNA species showed no qualitative differences in the elution profiles. However, leucyl-, lysyl- and valyl-tRNA species showed quantitative differences in the relative amounts of the isoaccepting species present in chloroplasts and etioplasts. The analysis of modified nucleotides of total tRNAs from the two plastid types indicated that total tRNA from etioplasts was undermodified with respect to ribothymidine, isopentenyladenosine/hydroxy-isopentenyladenosine, 1-methylguanosine and 2-o-methylguanosine. This indicates that illumination may cause de novo synthesis of chloroplast tRNA-modifying enzymes encoded for by nuclear genes leading to the formation of highly modified tRNAs in chloroplasts. Based on these results, we speculate that the observed decrease in levels of aminoacylation, variations in the relative amounts of certain isoacceptors, and differences in the electrophoretic mobilities of some extra tRNA spots in the etioplast total tRNAs as compared to chloroplast total tRNAs could be due to some partially undermodified etioplast tRNAs. Taken together, the data suggested that the light-induced transformation of etioplasts into chloroplasts is accompanied by increases in the relative levels of some functional chloroplast tRNAs by post transcriptional nucleotide modifications.

Primary Sequence That Determines the Functional Overlap between Mitochondrial Heat Shock Protein 70 Ssc1 and Ssc3 of Saccharomyces cerevisiae

The evolutionary diversity of the HSP70 gene family at the genetic level has generated complex structural variations leading to altered functional specificity and mode of regulation in different cellular compartments. By utilizing Saccharomyces cerevisiae as a model system for better understanding the global functional cooperativity between Hsp70 paralogs, we have dissected the differences in functional properties at the biochemical level between mitochondrial heat shock protein 70 (mtHsp70) Ssc1 and an uncharacterized Ssc3 paralog. Based on the evolutionary origin of Ssc3 and a high degree of sequence homology with Ssc1, it has been proposed that both have a close functional overlap in the mitochondrial matrix. Surprisingly, our results demonstrate that there is no functional cross-talk between Ssc1 and Ssc3 paralogs. The lack of in vivo functional overlap is due to altered conformation and significant lower stability associated with Ssc3. The substrate-binding domain of Ssc3 showed poor affinity toward mitochondrial client proteins and Tim44 due to the open conformation in ADP-bound state. In addition to that, the nucleotide-binding domain of Ssc3 showed an altered regulation by the Mge1 co-chaperone due to a high degree of conformational plasticity, which strongly promotes aggregation. Besides, Ssc3 possesses a dysfunctional inter-domain interface thus rendering it unable to perform functions similar to generic Hsp70s. Moreover, we have identified the critical amino acid sequence of Ssc1 and Ssc3 that can ``make or break'' mtHsp70 chaperone function. Together, our analysis provides the first evidence to show that the nucleotide-binding domain of mtHsp70s plays a critical role in determining the functional specificity among paralogs and orthologs across kingdoms.

On an exact populationary model of genetic algorithms

Genetic algorithms (GAs) are search methods that are being employed in a multitude of applications with extremely large search spaces. Recently, there has been considerable interest among GA researchers in understanding and formalizing the working of GAs. In an earlier paper, we have introduced the notion of binomially distributed populations as the central idea behind an exact ''populationary'' model of the large-population dynamics of the GA operators for objective functions called ''functions of unitation.'' In this paper, we extend this populationary model of GA dynamics to a more general class of objective functions called functions of unitation variables. We generalize the notion of a binomially distributed population to a generalized binomially distributed population (GBDP). We show that the effects of selection, crossover, and mutation can be exactly modelled after decomposing the population into GBDPs. Based on this generalized model, we have implemented a GA simulator for functions of two unitation variables-GASIM 2, and the distributions predicted by GASIM 2 match with those obtained from actual GA runs. The generalized populationary model of GA dynamics not only presents a novel and natural way of interpreting the workings of GAs with large populations, but it also provides for an efficient implementation of the model as a GA simulator. (C) Elsevier Science Inc. 1997.

Studies on the variations in compression strengths of graphite powders bearing glass-epoxy composites exposed to humid conditions

The moisture absorption and changes in compression strengths in glass-epoxy (G-E composites without and with discrete quantities of graphite powders introduced into the resin mix prior to its spreading on specific glass fabric (layers) during the lay-up (stacking) sequence forms the subject matter of this report. The results point to higher moisture absorption for graphite bearing specimens. The strengths of graphite-free coupons show a continuous decrease, while the filler bearing ones show an initial rise followed by a drop for larger exposure times. Scanning Fractographic features were examined for an understanding of the process. The observations were explained invoking the effect of matrix plasticizing and the role of interfacial regions.

Automatic decomposition of unstructured meshes employing genetic algorithms for parallel FEM computations

Parallel execution of computational mechanics codes requires efficient mesh-partitioning techniques. These mesh-partitioning techniques divide the mesh into specified number of submeshes of approximately the same size and at the same time, minimise the interface nodes of the submeshes. This paper describes a new mesh partitioning technique, employing Genetic Algorithms. The proposed algorithm operates on the deduced graph (dual or nodal graph) of the given finite element mesh rather than directly on the mesh itself. The algorithm works by first constructing a coarse graph approximation using an automatic graph coarsening method. The coarse graph is partitioned and the results are interpolated onto the original graph to initialise an optimisation of the graph partition problem. In practice, hierarchy of (usually more than two) graphs are used to obtain the final graph partition. The proposed partitioning algorithm is applied to graphs derived from unstructured finite element meshes describing practical engineering problems and also several example graphs related to finite element meshes given in the literature. The test results indicate that the proposed GA based graph partitioning algorithm generates high quality partitions and are superior to spectral and multilevel graph partitioning algorithms.

Identification of delamination in composite beams using spectral estimation and a genetic algorithm

An efficient strategy for identification of delamination in composite beams and connected structures is presented. A spectral finite-element model consisting of a damaged spectral element is used for model-based prediction of the damaged structural response in the frequency domain. A genetic algorithm (GA) specially tailored for damage identification is derived and is integrated with finite-element code for automation. For best application of the GA, sensitivities of various objective functions with respect to delamination parameters are studied and important conclusions are presented. Model-based simulations of increasing complexity illustrate some of the attractive features of the strategy in terms of accuracy as well as computational cost. This shows the possibility of using such strategies for the development of smart structural health monitoring softwares and systems.

Machine-tool selection and operation allocation in FMS: solving a fuzzy goal-programming model using a genetic algorithm

The decision-making process for machine-tool selection and operation allocation in a flexible manufacturing system (FMS) usually involves multiple conflicting objectives. Thus, a fuzzy goal-programming model can be effectively applied to this decision problem. The paper addresses application of a fuzzy goal-programming concept to model the problem of machine-tool selection and operation allocation with explicit considerations given to objectives of minimizing the total cost of machining operation, material handling and set-up. The constraints pertaining to the capacity of machines, tool magazine and tool life are included in the model. A genetic algorithm (GA)-based approach is adopted to optimize this fuzzy goal-programming model. An illustrative example is provided and some results of computational experiments are reported.

The repertoire of protein kinases encoded in the draft version of the human genome: atypical variations and uncommon domain combinations

Background: Phosphorylation by protein kinases is central to cellular signal transduction. Abnormal functioning of kinases has been implicated in developmental disorders and malignancies. Their activity is regulated by second messengers and by the binding of associated domains, which are also influential in translocating the catalytic component to their substrate sites, in mediating interaction with other proteins and carrying out their biological roles. Results: Using sensitive profile-search methods and manual analysis, the human genome has been surveyed for protein kinases. A set of 448 sequences, which show significant similarity to protein kinases and contain the critical residues essential for kinase function, have been selected for an analysis of domain combinations after classifying the kinase domains into subfamilies. The unusual domain combinations in particular kinases suggest their involvement in ubiquitination pathways and alternative modes of regulation for mitogen-activated protein kinase kinases (MAPKKs) and cyclin-dependent kinase (CDK)-like kinases. Previously unexplored kinases have been implicated in osteoblast differentiation and embryonic development on the basis of homology with kinases of known functions from other organisms. Kinases potentially unique to vertebrates are involved in highly evolved processes such as apoptosis, protein translation and tyrosine kinase signaling. In addition to coevolution with the kinase domain, duplication and recruitment of non-catalytic domains is apparent in signaling domains such as the PH, DAG-PE, SH2 and SH3 domains. Conclusions: Expansion of the functional repertoire and possible existence of alternative modes of regulation of certain kinases is suggested by their uncommon domain combinations. Experimental verification of the predicted implications of these kinases could enhance our understanding of their biological roles.

Nonlinear Dynamics of Beams with Stochastic Parameter Variations

The aim of this paper is to investigate the steady state response of beams under the action of random support motions. The study is of relevance in the context of earthquake response of extended land based structures such as pipelines and long span bridges, and, secondary systems such as piping networks in nuclear power plant installations. The following complicating features are accounted for in the response analysis: (a) differential support motions: this is characterized in terms of cross power spectral density functions associated with distinct support motions, (b) nonlinear support conditions, and (c) stochastically inhomogeneous stiffness and mass variations of the beam structure; questions on non-Gaussian models for these variations are considered. The method of stochastic finite elements is combined with equivalent linearization technique and Monte Carlo simulations to obtain response moments.