evidence of codon usage in the nearest neighbor spacing distribution of bases in bacterial genomes a b s t r a c t statistical analysis of whole genomic sequences usually assumes a homogeneous nucleotide density throughout the genome an assumption that has been proved incorrect for several organisms since the nucleotide density is only locally homogeneous to avoid giving a single numerical value to this variable property we propose the use of spectral statistics which characterizes the density of nucleotides as a function of its position in the genome we show that the cumulative density of bases in bacterial genomes can be separated into an average or secular plus a fluctuating part bacterial genomes can be divided into two groups according to the qualitative description of their secular part linear and piecewise linear these two groups of genomes show different properties when their nucleotide spacing distribution is studied in order to analyze genomes having a variable nucleotide density statistically the use of unfolding is necessary i e to get a separation between the secular part and the fluctuations the unfolding allows an adequate comparison with the statistical properties of other genomes with this methodology four genomes were analyzed burkholderia bacillus clostridium and corynebacterium interestingly the nearest neighbor spacing distributions or detrended distance distributions are very similar for species within the same genus but they are very different for species from different genera this difference can be attributed to the difference in the codon usage

self similarity and network perspective of the chinese fund market abstract by testing 88 different funds of the chinese fund market cfm we find fractal behavior and long range correlations in the return series which are insensitive to the kind of funds meanwhile a power law relationship between the deviation d of prices and the hurst exponent h has been obtained which may be useful for predicting the price time series in addition with funds being viewed as nodes and the connections among the funds being determined by the cross correlation coefficients using a winner takes all approach we investigate the topological properties of the fund network our analysis reveals that during different time periods the cumulative degree distributions of the fund network all obey the double power law format moreover the small world property is also found for the fund network

some aspects of fusion in estimation theory the problem of fusing or combining various estimates to obtain a single good estimate is investigated the problem of fusion in estimation theory is addressed and several examples using common distributions in which virtually any method of fusion would be useless in approximating the random variable of interest are presented a theorem which for a very general situation shows that fusion resulting in an almost surely exact approximation is always possible is presented in particular this result addresses the situation in which the data consists of the random variable of interest corrupted by additive gaussian noise and the random variable of interest could be any second order random variable finally an example which illustrates the utility of this result is presented

teaching learning based optimization with dynamic group strategy for global optimization abstract global optimization remains one of the most challenging tasks for evolutionary computation and swarm intelligence in recent years there have been some significant developments in these areas regarding the solution of global optimization problems in this paper we propose an improved teaching learning based optimization tlbo algorithm with dynamic group strategy dgs for global optimization problems different to the original tlbo algorithm dgstlbo enables each learner to learn from the mean of his corresponding group rather than the mean of the class in the teacher phase furthermore each learner employs the random learning strategy or the quantum behaved learning strategy in his corresponding group in the learner phase regrouping occurs dynamically after a certain number of generations helping to maintain the diversity of the population and discourage premature convergence to verify the feasibility and effectiveness of the proposed algorithm experiments are conducted on 18 numerical benchmark functions in 10 30 and 50 dimensions the results show that the proposed dgstlbo algorithm is an effective method for global optimization problems

correlation property of length sequences based on global structure of the complete genome this paper considers three kinds of length sequences of the complete genome detrended fluctuation analysis spectral analysis and the mean distance spanned within time l are used to discuss the correlation property of these sequences the values of the exponents from these methods of these three kinds of length sequences of bacteria indicate that the long range correlations exist in most of these sequences the correlations have a rich variety of behaviors including the presence of anti correlations furthermore using the exponent gamma it is found that these correlations are all linear gamma 1 0 0 03 it is also found that these sequences exhibit l f noise in some interval of frequency f l the length of this interval of frequency depends on the length of the sequence the shape of the periodogram in f l exhibits some periodicity the period seems to depend on the length and the complexity of the length sequence

recognition of an organism from fragments of its complete genome this paper considers the problem of matching a fragment to an organism using its complete genome our method is based on the probability measure representation of a genome we first demonstrate that these probability measures can be modeled as recurrent iterated function systems rifs consisting of four contractive similarities our hypothesis is that the multifractal characteristics of the probability measure of a complete genome as captured by the rifs is preserved in its reasonably long fragments we compute the rifs of fragments of various lengths and random starting points and compare with that of the original sequence for recognition using the euclidean distance a demonstration on five randomly selected organisms supports the above hypothesis

distributed estimators for nonlinear systems a nonlinear distributed estimation problem is solved by using reduced order local models using local models with lower dimensions than the observed process model will lessen the local processors complexities or computational loads fusion algorithms that combine local densities to construct the centralized density of a nonlinear random process are presented the local densities are generated at each measurement time and communicated to a coordinator the models used to produce these densities are reduced order valid models the validity of the local models guarantees that the coordinator reconstructs exactly the centralized density function

optimal linear estimation fusion i unified fusion rules this paper deals with data or information fusion for the purpose of estimation three estimation fusion architectures are considered centralized distributed and hybrid a unified linear model and a general framework for these three architectures are established optimal fusion rules based on the best linear unbiased estimation blue the weighted least squares wls and their generalized versions are presented for cases with complete incomplete or no prior information these rules are more general and flexible and have wider applicability than previous results for example they are in a unified form that is optimal for all of the three fusion architectures with arbitrary correlation of local estimates or observation errors across sensors or across time they are also in explicit forms convenient for implementation the optimal fusion rules presented are not limited to linear data models illustrative numerical results are provided to verify the fusion rules and demonstrate how these fusion rules can be used in cases with complete incomplete or no prior information

an alternative description of power law correlations in dna sequences abstract we analyze the coding sequence for the homo sapiens via a model which naturally embraces power law correlations plc among the bases in dna sequences of living organisms this model is based on a principle of universal optimization which is the core of all statistical arguments being associated with the power law distribution function of the length of dna measured in base pairs bp this distribution provides a plc parameter introduced through a nonadditive framework in which such parameter measures the plc in the dna sequence the results show that the short range correlations src always present in coding dna sequences are appropriately captured through the power law distribution adequately describing the cumulative length distribution of dna bases in contrast with the case of the traditional exponential statistical model we use an empirical cumulative distribution function and the database of proteins compiled by the ensembl project to show that the power law distribution provides the best description of the data a bayesian analysis of the data further confirms this result

combining and updating of local estimates and regional maps along sets of one dimensional tracks in this paper we consider the problem of combining and updating estimates that may have been generated in a distributed fashion or may represent estimates generated at different times of the same process sample path the first of these cases has applications in decentralized estimation while the second has applications in updating maps of spatially distributed random quantities given measurements along several tracks the method of solution for the second problem uses the result of the first and the similarity in the formulation and solution of these problems emphasizes the conceptual similarity between many problems in decentralized control and in the analysis of random fields

multifractals embedded in short time series an unbiased estimation of probability moment an exact estimation of probability moments is the base for several essential concepts such as the multifractals the tsallis entropy and the transfer entropy by means of approximation theory we propose a new method called factorial moment based estimation of probability moments theoretical prediction and computational results show that it can provide us an unbiased estimation of the probability moments of continuous order calculations on probability redistribution model verify that it can extract exactly multifractal behaviors from several hundred recordings its powerfulness in monitoring evolution of scaling behaviors is exemplified by two empirical cases i e the gait time series for fast normal and slow trials of a healthy volunteer and the closing price series for shanghai stock market by using short time series with several hundred lengths a comparison with the well established tools displays significant advantages of its performance over the other methods the factorial moment based estimation can evaluate correctly the scaling behaviors in a scale range about three generations wider than the multifractal detrended fluctuation analysis and the basic estimation the estimation of partition function given by the wavelet transform modulus maxima has unacceptable fluctuations besides the scaling invariance focused in the present paper the proposed factorial moment of continuous order can find its various uses such as finding nonextensive behaviors of a complex system and reconstructing the causality relationship network between elements of a complex system

measuring complexity nonextensivity and chaos in the dna sequence of the major histocompatibility complex abstract we analyze 4 mb sequences of the major histocompatibility complex mhc which is a dna segment on chromosome 6 with high gene density controlling many immunological functions and associated with many diseases the analysis is based on modern theoretical and mathematical tools of complexity theory such as nonlinear time series analysis and tsallis non extensive statistics the results revealed that the dna complexity and self organization can be related to fractional dynamical nonlinear processes with low dimensional deterministic chaotic and non extensive statistical character which generate the dna sequences under the extremization of tsallis q entropy principle while it still remains an open question as to whether the dna walk is a fractional brownian motion fbm a static anomalous diffusion process or a non gaussian dynamical fractional anomalous diffusion process the results of this study testify for the latter providing also a possible explanation for the previously observed long range power law correlations of nucleotides as well as the long range correlation properties of coding and non coding sequences present in dna sequences

factorial moments analyses show a characteristic length scale in dna sequences a unique feature of most of the dna sequences found through the factorial moments analysis is the existence of a characteristic length scale around which the density distribution is nearly poissonian above this point the dna sequences irrespective of their intron contents show long range correlations with a significant deviation from the gaussian statistics while below this point the dna statistics are essentially gaussian the famous dna walk representation is also shown to be a special case of the present analysis

distributed estimation algorithms for nonlinear systems in this paper we consider the problem of combining the local conditional distributions of a random variable which have been generated by local observers having access to their private information sufficient statistics for the local distributions are communicated to a coordinator who attempts to reconstruct the global centralized distribution using only the communicated statistics we obtain a distributed processing algorithm which recovers exactly the centralized conditional distribution the results can be applied in designing distributed hypothesis testing algorithms for event driven systems

teaching learning based optimization with global crossover for global optimization problems teaching learning based optimization tlbo is a newly developed population based meta heuristic algorithm it has better global searching capability but it also easily got stuck on local optima when solving global optimization problems this paper develops a new variant of tlbo called teaching learning based optimization with global crossover tlbo gc for improving the performance of tlbo in teaching phase a perturbed scheme is proposed to prevent the current best solution from getting trapped in local minima and a new global crossover strategy is incorporated into the learning phase which aims at balancing local and global searching effectively the performance of tlbo gc is assessed by solving global optimization functions with different characteristics compared to the tlbo several modified tlbos and other promising heuristic methods numerical results reveal that the tlbo gc has better optimization performance

nearest neighbor spacing distribution of basis in some intron less and intron containing dna sequences abstract we show that the nearest neighbor distribution of distances between basis pairs of some intron less and intron containing coding regions are the same when a procedure called unfolding is applied such a procedure consists in separating the secular variations from the oscillatory terms the form of the distribution obtained is quite similar to that of a random i e poissonian sequence this is done for the humbmyh7cd dromyonma humbmyh7 and dromhc sequences the first two correspond to highly coding regions while the last two correspond to non coding regions we also show that the distributions before the unfolding procedure depend on the secular part but after the unfolding procedure we obtain an striking result all distributions are similar to each other the result becomes independent of the content of introns or the species we have chosen this is in contradiction with the results obtained with the detrended fluctuation analysis in which the correlations yield different results for intron less and intron containing regions

statistical properties of dna sequences revisited the role of inverse bilateral symmetry in bacterial chromosomes abstract herein it is shown that in order to study the statistical properties of dna sequences in bacterial chromosomes it suffices to consider only one half of the chromosome because they are similar to its corresponding complementary sequence in the other half this is due to the inverse bilateral symmetry of bacterial chromosomes contrary to the classical result that dna coding regions of bacterial genomes are purely uncorrelated random sequences here it is shown via a renormalization group approach that dna random fluctuations of single bases are modulated by log periodic variations distance series of triplets display long range correlations in each half of the intact chromosome and in protein coding sequences or both long range correlations and log periodic modulations along the whole chromosome hence scaling analyses of distance series of dna sequences have to consider the functional units of bacterial chromosomes

ensemble of niching algorithms although niching algorithms have been investigated for almost four decades as effective procedures to obtain several good and diverse solutions of an optimization problem no effort has been reported on combining different niching algorithms to form an effective ensemble of niching algorithms in this paper we propose an ensemble of niching algorithms ena and illustrate the concept by an instantiation which is realized using four different parallel populations the offspring of each population is considered by all parallel populations the instantiation is tested on a set of 16 real and binary problems and compared against the single niching methods with respect to searching ability and computation time results confirm that ena method is as good as or better than the best single method in it on every test problem moreover comparison with other state of the art niching algorithms demonstrates the competitiveness of our proposed ena

measure representation and multifractal analysis of complete genomes this paper introduces the notion of measure representation of dna sequences spectral analysis and multifractal analysis are then performed on the measure representations of a large number of complete genomes the main aim of this paper is to discuss the multifractal property of the measure representation and the classification of bacteria from the measure representations and the values of the dq spectra and related cq curves it is concluded that these complete genomes are not random sequences in fact spectral analyses performed indicate that these measure representations considered as time series exhibit strong long range correlation here the long range correlation is for the k strings with dictionary ordering and it is different from the base pair correlations introduced by other people for substrings with length k58 the dq spectra of all organisms studied are multifractal like and sufficiently smooth for the cq curves to be meaningful with the decreasing value of k the multifractality lessens the cq curves of all bacteria resemble a classical phase transition at a critical point but the analogous phase transitions of chromosomes of nonbacteria organisms are different apart from chromosome 1 of c elegans they exhibit the shape of double peaked specific heat function a classification of genomes of bacteria by assigning to each sequence a point in two dimensional space d21 d1 and in three dimensional space d21 d1 d22 was given bacteria that are close phylogenetically are almost close in the spaces d21 d1 and d21 d1 d22

multifractal characterisation of length sequences of coding and noncoding segments in a complete genome the coding and noncoding length sequences constructed from a complete genome are characterised by multifractal analysis the dimension spectrum dq and its derivative the analogous specific heat cq are calculated for the coding and noncoding length sequences of bacteria where q is the moment order of the partition sum of the sequences from the shape of the dq and cq curves it is seen that there exists a clear difference between the coding noncoding length sequences of all organisms considered and a completely random sequence the complexity of noncoding length sequences is higher than that of coding length sequences for bacteria almost all dq curves for coding length sequences are flat so their multifractality is small whereas almost all dq curves for noncoding length sequences are multifractal like it is seen that the analogous specific heats of noncoding length sequences of bacteria have a rich variety of behaviour which is much more complex than that of coding length sequences we propose to characterise the bacteria according to the types of the cq curves of their noncoding length sequences this new type of classification allows a better understanding of the relationship among bacteria at the global gene level instead of nucleotide sequence level

a note on teaching learning based optimization algorithm teaching learning based optimization tlbo seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances it is reported that it outperforms some of the well known metaheuristics regarding constrained benchmark functions constrained mechanical design and continuous non linear numerical optimization problems such a breakthrough has steered us towards investigating the secrets of tlbo s dominance this paper reports our findings on tlbo qualitatively and quantitatively through code reviews and experiments respectively our findings have revealed three important mistakes regarding tlbo 1 at least one unreported but important step 2 incorrect formulae on a number of fitness function evaluations and 3 misconceptions about parameter less control additionally unfair experimental settings conditions were used to conduct experimental comparisons e g different stopping criteria the experimental results for constrained and unconstrained benchmark functions under fairly equal conditions failed to validate its performance supremacy the ultimate goal of this paper is to provide reminders for metaheuristics researchers and practitioners in order to avoid similar mistakes regarding both the qualitative and quantitative aspects and to allow fair comparisons of the tlbo algorithm to be made with other metaheuristic algorithms

distributed estimation constraints on the choice of the local models the following estimation problem is considered a coordinator must reconstruct the global probability density of a nonlinear random process conditioned on the noise corrupted observation history the coordinator can only access the local probability density produced by local processing of the observation history using a local model different from the process model it is shown that if the local model satisfies an algebraic constraint the coordinator can reconstruct the same conditional density of the state process as the one obtained if the observations were processed using the coordinator process model it is assumed that the random process is a nonlinear stochastic differential equation driven by a brownian motion and the observation process is corrupted by additive brownian motion which is identically modeled by the coordinator and the local processor

dynamic mentoring and self regulation based particle swarm optimization algorithm for solving complex real world optimization problems abstract in this paper a dynamic mentoring scheme along with a self regulation scheme have been incorporated in the standard particle swarm optimization pso algorithm to empower the searching particles with human like characteristics the algorithm is referred to as a dynamic mentoring and self regulation based particle swarm optimization dmesr pso algorithm based on their experiences the particles are divided into three groups viz the mentor group the mentee group and the independent learner group where the number of particles in each group is dynamically changing in every iteration in human learning psychology mentoring is regarded as a powerful and effective learning process and independent learners are the ones who do not need mentoring and are capable of performing self regulation of their own knowledge therefore the particles in each of the above three groups have different learning strategies for their velocity updates where the mentors are equipped with a strong self belief based search the mentees are taking guidance from the mentors and the independent learners employ self perception strategy the dmesr pso algorithm has been extensively evaluated using the simple unimodal and multimodal benchmark functions from cec2005 more complex shifted and rotated benchmark functions from cec2013 and also based on eight real world problems from cec2011 the results have been compared with six state of the art pso variants and five meta heuristic algorithms for the cec2005 problems further a comparative analysis on cec2013 benchmark functions with different pso variants has also been presented finally dmesr pso s performance on the real world problems is compared with the top two algorithms from the cec2011 competition the results indicate that the proposed learning strategies help dmesr pso to achieve faster convergence and provide better solutions in most of the problems with a 95 confidence level yielding an effective optimization algorithm for real world applications