Effective Fitness as an Alternative Paradigm for Evolutionary Computation I: General Formalism

In evolutionary computation the concept of a fitness landscape has played an important role, evolution itself being portrayed as a hill-climbing process on a rugged landscape. In this article we review the recent development of an alternative paradigm for evolution on a fitness landscape—effective fitness. It is shown that in general, in the presence of other genetic operators such as mutation and recombination, hill-climbing is the exception rather than the rule; a discrepancy that has its origin in the different ways in which the concept of fitness appears—as a measure of the number of fit offspring, or as a measure of the probability to reach reproductive age. Effective fitness models the former not the latter and gives an intuitive way to understand population dynamics as flows on an effective fitness landscape when genetic operators other than reproductive selection play an important role. Additionally, we will show that when the genotype-phenotype map is degenerate, i.e. there exists a synonym symmetry, it can be used to quantify the degree of symmetry breaking of the map, thus allowing for a quantitative explanation of phenomena such as self-adaptation, bloat and evolutionary robustness.     

语言建模中最小化样本风险算法的研究和改进

目前,一些主流的判别学习算法只能优化光滑可导的损失函数,但在自然语言处理(natural language processing,简称NLP)中,很多应用的直接评价标准(如字符转换错误数(character error rate,简称CER))都是不可导的阶梯形函数.为解决此问题,研究了一种新提出的判别学习算法--最小化样本风险(minimum sample risk,简称MSR)算法.与其他判别训练算法不同,MSR算法直接使用阶梯形函数作为其损失函数.首先,对MSR算法的时空复杂性作了分析和提高;同时,提出了改进的算法MSR-II,使得特征之间相关性的计算更加稳定.此外,还通过大量领域适应性建模实验来考察MSR-II的鲁棒性.日文汉字输入实验的评测结果表明:(1) MSR/MSR-II显著优于传统三元模型,使错误率下降了20.9%;(2) MSR/MSR-II与另两类主流判别学习算法Boosting和Perceptron表现相当;(3) MSR-II不仅在时空复杂度上优于MSR,特征选择的稳定性也更高;(4) 领域适应性建模的结果证明了MSR-II的良好鲁棒性.总之,MSR/MSR-II是一种非常有效的算法.由于其使用的是阶梯形的损失函数,因此可以广泛应用于自然语言处理的各个领域,如拼写校正和机器翻译.     

Solutions of the BCM learning rule in a network of lateral interacting nonlinear neurons.

We introduce a new method for obtaining the fixed points for neurons that follow the BCM learning rule. The new formalism, which is based on the objective function formulation, permits analysis of a laterally connected network of nonlinear neurons and allows explicit calculation of the fixed points under various network conditions. We show that the stable fixed points, in terms of the postsynaptic activity, are not altered by the lateral connectivity or nonlinearity. We show that the lateral connectivity alters the probability of attaining different states in a network of interacting neurons. We further show the exact alteration in presynaptic weights as a result of the neuronal nonlinearity.     

Euro Area Deflationary Pressure Index

We focus on forecasting the probability that euro-area inflation will fall into one of three intervals by employing an ordered multinomial model augmented with macroeconomic variables. We directly forecast the probability that the expected euro area HICP price index inflation rate (12-month percent changes) over the next 12 and 24 months will be less than 1.5 percent, exceed 2 percent, or be between these two values. The model includes many predictors, and deal with dimensionality issues by an approach which mixes factor models with Bayesian shrinkage. Our results show that macroeconomic variables’ inclusion improves the model’s forecast quality, especially at the longer horizon considered. The Deflationary Pressure Index coincides with the probability that inflation is below 1.5 percent on average in the next 24 months, and it is useful as a policy monitoring tool.

     

ON STOCHASTIC CONVERGENCE THEOREMS FOR THE FUZZY C-MEANS CLUSTERING PROCEDURE∗

The fuzzy c-means clustering algorithm has been well studied for equal weight distributions on a finite set. Suppose that this situation is generalized to an arbitrary probability distribution on a finite dimensional Euclidean space, assuming that the second moment of the distribution is finite. Now choose ever larger finite random samples from this distribution and compute the standard optimal membership functions for a fuzzy partition into c clusters. Then the convergence of the cluster center points is established in the Hausdorff sense with probability one, provided that there is a unique optimal center point set. These optimal center points are the fixed point of a simple operator, and there is a corresponding iterative algorithm that generalizes the usual procedure.     

基于主动形状模型算法的局部灰度模型的加权改进方法

主动形状模型(ASM)算法在对目标点搜索的过程中,只采用了标定点周围的局部灰度信息,这样往往会使灰度相似但其实微观纹理细节差别很大的两个点混为一谈,最终使算法的定位不够精确。为此,提出一种基于主动形状模型算法的局部灰度模型的加权改进方法,该算法采用一系列服从离散高斯分布的加权系数依次表示标定点法线方向上两侧的候选点是真实特征点的可能性,从而建立局部加权灰度模型。通过实验结果表明,改进算法比传统算法精度提高了6%。     

基于MSR理论的一种有效的图像增强算法

针对受光照影响造成图像低对比度不清晰的特点,本文根据多尺度Retinex理论的原理,构造了一个有效的平滑传导函数,并结合MSR的优点,提出一个多维多尺度的图像处理算法。仿真结果表明,该算法处理图像的效果要明显优于经典的直方图均衡、同态滤波和MSR等方法。并且该算法对不同特点的图像有着广泛的适应性。     

Information technology and the welfare cost of anticipated inflation

I numerically study inflation’s welfare cost in a model in which there are two ways of mediating trade: money and information technology (IT), a probabilistically updated public record of agents’ histories. I find that a higher updating probability either brings the incentive-constrained output closer to its unconstrained value, or triggers the abandonment of money. In the first case the higher updating probability induces both higher inflation and a lower welfare cost of inflation. In the second case, welfare is higher than with the lower updating probability, but inflation’s welfare cost measured in a standard way is also higher.      

Distribution of Lattice Points

We discuss the lattice structure of congruential random number generators and examine figures of merit. Distribution properties of lattice measures in various dimensions are demonstrated by using large numerical data. Systematic search methods are introduced to diagnose multiplier areas exhibiting good, bad and worst lattice structures. We present two formulae to express multipliers producing worst and bad laice points. The conventional criterion of normalised lattice rule is also questioned and it is shown that this measure used with a fixed threshold is not suitable for an effective discrimination of lattice structures. Usage of percentiles represents different dimensions in a fair fashion and provides consistency for different figures of merits.     

Motion codeword generation using selective subsequence clustering for human action recognition

The understanding of human activity is one of the key research areas in human-centered robotic applications. In this paper, we propose complexity-based motion features for recognizing human actions. Using a time-series-complexity measure, the proposed method evaluates the amount of useful information in subsequences to select meaningful temporal parts in a human motion trajectory. Based on these meaningful subsequences, motion codewords are learned using a clustering algorithm. Motion features are then generated and represented as a histogram of the motion codewords. Furthermore, we propose a multiscaled sliding window for generating motion codewords to solve the sensitivity problem of the performance to the fixed length of the sliding window. As a classification method, we employed a random forest classifier. Moreover, to validate the proposed method, we present experimental results of the proposed approach based on two open data sets: MSR Action 3D and UTKinect data sets.     

Particle swarm optimization with mutation operations controlled by landscape modality estimation using hill-valley detection

Particle swarm optimization (PSO) is one of swarm intelligence algorithms and has been used to solve various optimization problems. Since the performance of PSO is much affected by the algorithm parameters of PSO, studies on adaptive control of the parameters have been done. Adaptive PSO (APSO) is one of representative studies. Parameters are controlled according to the evolutionary state, where the state is estimated by distance relations among a best search point and other search points. Also, a global Gaussian mutation operation is introduced to escape from local optima. In this study, a new adaptive control based on landscape modality estimation using hill-valley detection is proposed. A proximity graph is created from search points, hills and valleys are detected in the graph, landscape modality of an objective function is identified as unimodal or multimodal. Parameters are adaptively controlled as: parameters for convergence are selected in unimodal landscape and parameters for divergence are selected in multimodal landscape. Also, two mutation operations are introduced according to the modality. In unimodal landscape, a new local mutation operation is applied to the worst hill point which will be moved toward the best point for convergence. In multimodal landscape, a new adaptive global mutation operation is applied to all hill points for escaping from local optima. The advantage of the proposed method is shown by comparing the results of the method with those by PSO with fixed parameters and APSO.     

Machine learning models for mathematical symbol recognition: A stem to stern literature analysis

Given the ubiquity of handwriting and mathematical content in human transactions, machine recognition of handwritten mathematical text and symbols has become a domain of great practical scope and significance. Recognition of mathematical expression (ME) has remained a challenging and emerging research domain, with mathematical symbol recognition (MSR) as a requisite step in the entire recognition process. Many variations in writing styles and existing dissimilarities among the wide range of symbols and recurring characters make the recognition tasks strenuous even for Optical Character Recognition. The past decade has witnessed the emergence of recognition techniques and the peaking interest of several researchers in this evolving domain. In light of the current research status associated with recognizing handwritten math symbols, a systematic review of the literature seems timely. This article seeks to provide a complete systematic analysis of recognition techniques, models, datasets, sub-stages, accuracy metrics, and accuracy details in an extracted form as described in the literature. A systematic literature review conducted in this study includes pragmatic studies until the year 2021, and the analysis reveals Support Vector Machine (SVM) to be the most dominating recognition technique and symbol recognition rate to be most frequently deployed accuracy measure and other interesting results in terms of segmentation, feature extraction and datasets involved are vividly represented. The statistics of mathematical symbols-related papers are shown, and open problems are identified for more advanced research. Our study focused on the key points of earlier research, present work, and the future direction of MSR.

     

基于多尺度Retinex算法的遥感图像增强

大多数的遥感图像存在视觉对比度低、分辨率低的缺点．因而在对遥感图像分析之前,通常都需要通过遥感图像增强技术对图像进行增强处理。当前的图像增强方法有很多种,文中引入了一种新颖的图像增强方法,即多尺度Rednex（MSR）算法。这种增强技术尤其对能见度差、分辨率低的图像有很好的效果,因此适合于对遥感图像的增强处理。此外,还引入了几种常用的经典图像增强方法,如直方图均衡法等。为了证实所引入的MSR算法对遥感图像的增强效果优于其他的增强方法,在实验中,将经典的图像增强方法和MSR算法分别应用于增强一幅典型的遥感图像并对比增强后的增强效果。实验结果表明MSR算法在对遥感图像的增强中可以取得满意的效果并且优于其他的图像增强方法。     

Convergence and Consistency of Fuzzy c-means/ISODATA Algorithms

The fuzzy c-means/ISODATA algorithm is usually described in terms of clustering a finite data set. An equivalent point of view is that the algorithm clusters the support points of a finite-support probability distribution. Motivated by recent work on the hard version of the algorithm, this paper extends the definition to arbitrary distributions and considers asymptotic properties. It is shown that fixed points of the algorithm are stationary points of the fuzzy objective functional, and vice versa. When the algorithm is iteratively applied to an initial prototype set, the sequence of prototype sets produced approaches the set of fixed points. If an unknown distribution is approximated by the empirical distribution of stationary, ergodic observations, then as the number of observations grows large, fixed points of the algorithm based on the empirical distribution approach fixed points of the algorithm based on the true distribution. Furthermore, with respect to minimizing the fuzzy objective functional, the algorithm based on the empirical distribution is asymptotically at least as good as the algorithm based on the true distribution.     

Controlling the losing probability in a monotone game

We deal with a complex game between Alice and Bob where each contender’s probability of victory grows monotonically by unknown amounts with the resources employed. For a fixed effort on Alice’s part, Bob increases his resources on the basis of the results for each round (victory, tie or defeat) with the aim of reducing the probability of defeat to below a given threshold. We read this goal in terms of computing a confidence interval for the probability of losing and realize that the moves in some contests may bring in an indeterminacy trap: in certain games Bob cannot simultaneously have both a low probability-of-defeat measure and a narrow confidence interval. We use the inferential mechanism called twisting argument to compute the above interval on the basis of two joint statistics. Careful use of such statistics allows us to avoid indeterminacy.     

Estimating parameters of forest patch transition models from gap models

An algorithm to estimate the parameter values of a transition forest landscape model (MOSAIC) from a gap model (FACET) is presented here. MOSAIC is semi-Markov; it includes random distributed holding times and fixed or deterministic delays in addition to transition probabilities. FACET is a terrain-sensitive version of ZELIG, a spatially explicit gap model. For each topographic class, the input to the algorithm consists of gap model tracer files identifying the cover type of each plot through time. These cover types or states are defined a priori. The method, based on individual plots of the FACET model, requires one FACET run initialized from the “gap” cover type and follows the time history of each plot. The algorithm estimates the transition probability by counting the number of transitions between each pair of states and estimates the fixed lags and the parameters of the probability density functions of the distributed delays by recording the times at which these transitions are made. These density functions are assumed to be Erlang; its two parameters, order and rate, are estimated using a nonlinear least squares procedure. Thus, as output, the algorithm produces four matrices at each terrain class: transition probabilities, fixed delays, and the two parameters for the Erlang distributions. The algorithm is illustrated by its application to two sites, high and low elevation, from the H.J. Andrews Forest in the Oregon Cascades. This scaling-up method helps to bridge the conceptual breach between landscape- and stand-scale models. To reflect landscape heterogeneity, the algorithm can be executed repetitively for many different terrain classes. While the method developed here focuses on FACET and MOSAIC, this general approach could be extended to use other fine-scale models or other forms of meta-models.     

Which Econometric Specification to Characterize the U.S. Inflation Rate Process?

The current study takes place in a univariate context and we seek to determine an econometric model leading to best characterize the U.S. inflation rate dynamic. In order to achieve this, three types of specifications, associated with three possible evolutions of the expected rate are considered. The first allows an overall instability of the trend or the expected inflation rate. The second considers an alternative specification in which the expected inflation rate is unstable in periodic segments of the sample. Finally, the last specification allows instability of a “mixed” type in which the trend inflation rate is assumed to be random or subject to a probability schema. The results of our study indicate that this last specification is the one that gives the most adequate characterization of the inflation rate dynamic. The inflation rate thus appears generated by a second order autoregressive process with, on the one hand, unchanging lag coefficients and, on the other, a unconditional mean which switches between three globals different frequency regimes of accession.     

A Network Analysis of the United Kingdom’s Consumer Price Index

In this paper we model the United Kingdom’s Consumer Price Index as a complex network and we apply clustering and optimization techniques to study the network evolution through time. By doing this, we provide a dynamic, multi-level analysis of the mechanism that drives inflation in the U.K. We find that the CPI classes’ network exhibits an evolving topology through time which depends substantially on the prevailing economic conditions in the U.K. We identify non-overlapping communities of these CPI classes and we observe that they do not correspond to the actual categories they belong to; a finding that suggests that diverse forces are driving the inter-relations of the CPI classes which are stronger between categories rather than within them. Finally, we construct a reduced version of the U.K. CPI that fulfills the core inflation measure criteria and can possibly be used as an appropriate measure of the underlying inflation in the U.K. Since this new measure makes use of only 14 out of the 85 U.K. CPI classes, it can be used to complement the Bank of England’s arsenal of core inflation measures without the need for further resource allocation.     

Performance evaluation of an importance sampling technique in a Jackson network

Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.     

Shortest distance and reliability of probabilistic networks

When the “length” of a link is not deterministic and is governed by a stochastic process, the “shortest” path between two points in the network is not necessarily always composed of the same links and depends on the state of the network. For example, in communication and transportation networks, the travel time on a link is not deterministic and the fastest path between two points is not fixed. This paper presents an algorithm to compute the expected shortest travel time between two nodes in the network when the travel time on each link has a given independent discrete probability distribution. The algorithm assumes the knowledge of all the paths between two nodes and methods to determine the paths are referenced.In reliability (i.e. the probability that two given points are connected by a path) computations, associated with each link is a probability of “failure” and a probability of “success”. Since “failure” implies infinite travel time, the algorithm simultaneously computes reliability. The paper also discusses the algorithm's capability to simultaneously compute some other performance measures which are useful in the analysis of emergency services operating on a network.