Red Queen strange attractors in host–parasite replicator gene-for-gene coevolution

We study a continuous time model describing gene-for-gene, host–parasite interactions among self-replicating macromolecules evolving in both neutral and rugged fitness landscapes. Our model considers polymorphic genotypic populations of sequences with 3 bits undergoing mutation and incorporating a “type II” non-linear functional response in the host–parasite interaction. We show, for both fitness landscapes, a wide range of chaotic coevolutionary dynamics governed by Red Queen strange attractors. The analysis of a rugged fitness landscape for parasite sequences reveals that fittest genotypes achieve lower stationary concentration values, as opposed to the flattest ones, which undergo a higher stationary concentration. Our model also shows that the increase of parasites pressure (higher self-replication and mutation rates) generically involves a simplification of the host–parasite dynamical behavior, involving the transition from a chaotic to an ordered coevolutionary phase. Moreover, the same transition can also be found when hosts “run” faster through the hypercube. Our results, in agreement with previous studies in host–parasite coevolution, suggest that chaos might be common in coevolutionary dynamics of changing self-replicating entities undergoing a host–parasite ecology.     

Neutrality in fitness landscapes

The interplay of ruggedness and neutrality in fitness landscapes plays an important role in explaining the dynamics of evolutionary adaptation. While various measures of ruggedness (correlation functions, adaptive walks, or the density of local optima) are reasonably well understood, and models for constructing landscapes with a desired degree of ruggedness are readily available, very little is known about neutrality. We introduce the notion of additive random landscapes as a framework for tuning both neutrality and ruggedness at once, and we develop a formalism that allows the explicit computation of the most salient parameters that are associated with neutrality in landscapes of this type.     

Canonical approximation of fitness landscapes

We present a method for approximating a fitness landscape as a superposition of “elementary” landscapes. Given a correlation function of the landscape in question we show that the relative amplitudes of contributions with p-ary interactions can be computed. We show an application to RNA free energy landscapes. © 1996 John Wiley & Sons, Inc.     

分布估计算法中适应度函数模型检验及应用

在一种基于马尔可夫网络的分布估计算法中,利用解的适应度函数模型表示变量的概率分布.通过相关系数检验建立了一种衡量适应度函数模型有效性的方法,同时该方法也可以用来确定初始种群规模大小.在二维相关变量结构下,将该方法应用于一种多变量生物动态模型的优化问题,结论表明通过该方法可以选择适当的种群规模,保证适应度函数模型的有效性,并提高算法的优化性能.     

Mapping the global structure of TSP fitness landscapes

The global structure of combinatorial landscapes is not fully understood, yet it is known to impact the performance of heuristic search methods. We use a so-called local optima network model to characterise and visualise the global structure of travelling salesperson fitness landscapes of different classes, including random and structured real-world instances of realistic size. Our study brings rigour to the characterisation of so-called funnels, and proposes an intensive and effective sampling procedure for extracting the networks. We propose enhanced visualisation techniques, including 3D plots and the incorporation of colour, sizes and widths, to reflect relevant aspects of the search process. This brings an almost tangible new perspective to the landscape and funnel metaphors. Our results reveal a much richer global structure than the suggestion of a ‘big-valley’ structure. Most landscapes of the tested instances have multiple valleys or funnels; and the number, disposition and interaction of these funnels seem to relate to search difficulty on the studied landscapes. We also find that the structured TSP instances feature high levels of neutrality, an observation not previously reported in the literature. We then propose ways of analysing and visualising these neutral landscapes.     

Working principles,behavior, and performance of MOEAs on MNK-landscapes

This work studies the working principles, behavior, and performance of multiobjective evolutionary algorithms (MOEAs) on multiobjective epistatic fitness functions with discrete binary search spaces by using MNK-landscapes. First, we analyze the structure and some of the properties of MNK-landscapes under a multiobjective perspective by using enumeration on small landscapes. Then, we focus on the performance and behavior of MOEAs on large landscapes. We organize our study around selection, drift, mutation, and recombination, the four major and intertwined processes that drive adaptive evolution over fitness landscapes. This work clearly shows pros and cons of the main features of MOEAs, gives a valuable guide for the practitioner on how to set up his/her algorithm, enhance MOEAs, and presents useful insights on how to design more robust and efficient MOEAs.     

Coevolution of lexicon and syntax from a simulation perspective

Whether simple syntax (in the form of simple word order) can emerge during the emergence of lexicon is studied from a simulation perspective; a multiagent computational model is adopted to trace a lexicon‐syntax coevolution through iterative communications. Several factors that may affect this self‐organizing process are discussed. An indirect meaning transference is simulated to study the effect of nonlinguistic information in listener's comprehension. Besides the theoretical and empirical argumentations, this computational model, following the Emergentism, demonstrates an adaptation of syntax from some domain‐general abilities, which provides an argumentation against the Innatism. © 2005 Wiley Periodicals, Inc. Complexity 10: 50–62, 2005     

A genotype-phenotype-fitness assessment protocol for evolutionary self-assembly Wang tiles design

In a previous work we have reported on the evolutionary design optimisation of self-assembling Wang tiles capable of arranging themselves together into a target structure. Apart from the significant findings on how self-assembly is achieved, nothing has been yet said about the efficiency by which individuals were evolved. Specially in light that the mapping from genotype to phenotype and from this to fitness is clearly a complex, stochastic and non-linear relationship. One of the most common procedures would suggest running many experiments for different configurations followed by a fitness comparison, which is not only time-consuming but also inaccurate for such intricate mappings. In this paper we aim to report on a complementary dual assessment protocol to analyse whether our genetic algorithm, using morphological image analyses as fitness function, is an effective methodology. Thus, we present here fitness distance correlation to measure how effectively the fitness of an individual correlates to its genotypic distance to a known optimum, and introduce clustering as a mechanism to verify how the objective function can effectively differentiate between dissimilar phenotypes and classify similar ones for the purpose of selection.     

The dynamics of athletic performance,fitness and fatigue

We provide a mathematical dynamic model of athletic performance, fitness and fatigue based on the two well-known principles ‘train to failure’ and ‘use it or lose it’. The anabolic and catabolic processes are modelled with differential equations. Fitness is defined as muscle fitness. We model the work power of any muscle or set of muscles, and the muscle's maximum work power. Parameters are estimated and we present analytical and numerical results. The relationships between performance, fitness and fatigue are demonstrated for various activity scenarios. For example, the model quantifies the exact manner in which the optimal rest period can be determined to maximize the performance on a given day. The model provides realistic predictions, and constitutes a powerful tool which describes the processes by which performance, fitness and fatigue can be regulated and controlled.     

Quantum mechanics formalism for biological evolution

We study the evolution of sexual and asexual populations in fitness landscapes compatible with epistatic interactions. We find intriguing relations between the mathematics of biological evolution and quantum mechanics formalism. We give the general structure of the evolution of sexual and asexual populations which is in general an off-equilibrium process that can be expressed by path integrals over phylogenies. These phylogenies are the sum of linear lineages for asexual populations. For sexual populations, instead, each lineage is a tree of branching ratio two and the path integral describing the evolving population is given by a sum over these trees. Finally we show that the Bose–Einstein and the Fermi–Dirac distributions describe the stationary state of biological populations in simple cases.     

上海基础房价相关指标及走势的分析与研究

基础房价的相关指标及其走势一直是大众关心的热门话题．本文通过对上海基础房价相关指标的分析，建立了市场房价走势的两个数学模型．模型一：在相关性分析的基础上利用主成分分析消除指标间的共线性，再用回归拟合房价模型并进行预测；模型二：在相关性分析的基础上利用核估计方法预测出房价．继呵对2005年下半年的房价走势进行了预测，得出的结果与实际情况相吻合．     

Inclusion of Manufacturing Induced Uncertainties into Linear Vibration of Structures

Owing to manufacturing, composite materials and others can show considerable uncertainties in wall-thickness, fluctuations in material properties and other parameters, which are spatially distributed over the structure. These uncertainties have a random character and they cannot be reduced by mesh refinement within the finite element (FE) model. So what we need is a suitable statistical approach to describe the parameter changing that holds for the statistics of the process and the correlation between the parameter spatially distributed over the structure. The paper presents a solution for a spatial correlated simulation of parameter distribution owing to the manufacturing process or other causes that is suitable to be included in the finite element analysis (FEA). The spatial variation of parameters is modelled using variogram approach and it has been applied into FEA. For example the effect of spatial variation of thickness and the effect of spatial variation of material properties has been studied in this part of work. The effect of thickness on buckling has been also studied. The results could be used to asses the robustness of the structure and to get limits for manufacturing induced uncertainties. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Fast Fourier Transform for Fitness Landscapes

We cast some classes of fitness landscapes as problems of spectral analysis on various Cayley graphs. In particular, landscapes derived from RNA folding are realized on Hamming graphs and analyzed in terms of Walsh transforms; assignment problems are interpreted as functions on the symmetric group and analyzed in terms of the representation theory of S_n. We show that explicit computation of the Walsh/Fourier transforms is feasible for landscapes with up to 10⁸ configurations using fast Fourier transform techniques. We find that the cost function of a linear sum assignment problem involves only the defining representation of the symmetric group, while quadratic assignment problems are superpositions of the representations indexed by the partitions (n), (n−1,1), (n−2,2), and (n−2,1,1). These correspond to the four smallest eigenvalues of the Laplacian of the Cayley graph obtained by using transpositions as the generating set on S_n.     

A memetic Differential Evolution approach in noisy optimization

This paper proposes a memetic approach for solving complex optimization problems characterized by a noisy fitness function. The proposed approach aims at solving highly multivariate and multi-modal landscapes which are also affected by a pernicious noise. The proposed algorithm employs a Differential Evolution framework and combines within this three additional algorithmic components. A controlled randomization of scale factor and crossover rate are employed which should better handle uncertainties of the problem and generally enhance performance of the Differential Evolution. Two combined local search algorithms applied to the scale factor, during offspring generation, should enhance performance of the Differential Evolution framework in the case of multi-modal and high dimensional problems. An on-line statistical test aims at assuring that only strictly necessary samples are taken and that all pairwise selections are properly performed. The proposed algorithm has been tested on a various set of test problems and its behavior has been studied, dependent on the dimensionality and noise level. A comparative analysis with a standard Differential Evolution, a modern version of Differential Evolution employing randomization of the control parameters and four metaheuristics tailored to optimization in a noisy environment has been carried out. One of these metaheuristics is a classical algorithm for noisy optimization while the other three are modern Differential Evolution based algorithms for noisy optimization which well represent the state-of-the-art in the field. Numerical results show that the proposed memetic approach is an efficient and robust alternative for various and complex multivariate noisy problems and can be exported to real-world problems affected by a noise whose distribution can be approximated by a Gaussian distribution.     

Studying the emerging global brain: Analyzing and visualizing the impact of co‐authorship teams

This article introduces a suite of approaches and measures to study the impact of co‐authorship teams based on the number of publications and their citations on a local and global scale. In particular, we present a novel weighted graph representation that encodes coupled author‐paper networks as a weighted co‐authorship graph. This weighted graph representation is applied to a dataset that captures the emergence of a new field of science and comprises 614 articles published by 1036 unique authors between 1974 and 2004. To characterize the properties and evolution of this field, we first use four different measures of centrality to identify the impact of authors. A global statistical analysis is performed to characterize the distribution of paper production and paper citations and its correlation with the co‐authorship team size. The size of co‐authorship clusters over time is examined. Finally, a novel local, author‐centered measure based on entropy is applied to determine the global evolution of the field and the identification of the contribution of a single author's impact across all of its co‐authorship relations. A visualization of the growth of the weighted co‐author network, and the results obtained from the statistical analysis indicate a drift toward a more cooperative, global collaboration process as the main drive in the production of scientific knowledge. © 2005 Wiley Periodicals, Inc. Complexity 10: 57–67, 2005     

Application research on FSDM-based GA in optimizing curriculum schedule model in universities

In this paper, we first analyze the relationship between curricula, teachers, classes, time slices and classrooms in a graph. Then on the basis of constraint conditions in curriculum schedule practically in universities, we presents its optimization model, in which the fuzzy synthetic decision-making (FSDM) is used to optimize genetic algorithm (GA), and a new GA encoding scheme is employed to design fitness and punishment functions for the curriculum schedule problem. This model effectively improved a running performance, which provides a better implementation approach to improvements of the existing curriculum schedule systems. The experimental results show that fitness values of the FSDM-based GA are of obvious evolutional tendency, the chromosome encoding scheme and the fitness function can meet its requirements preferably, and the more adequate computation resources, the greater possibilities of no restoration for the obtained optimal individual.     

Organizational sticking points on NK Landscapes

Scholars studying human organizations have recently adopted the notion of fitness landscapes, a concept pioneered in the biological and physical sciences. Such scholars have generally assumed that organizations will migrate toward the local peaks of these landscapes, as biological and physical entities do. We use an agent‐based simulation to show, to the contrary, that a hierarchical human organization may very well come to rest at a “sticking point” that is not a local peak on the fitness landscape of the overall organization. Three pervasive features of human organizations create the distinction between sticking points and local peaks: the delegation of choices to separate decision makers, interdependencies between the domains of those decision makers, and differences between local incentives and global incentives. Our results illustrate both that it is valuable to use tools developed to study one type of complex adaptive system in order to examine another type and that researchers must adapt the tools with care as they attempt to do so. © 2002 Wiley Periodicals, Inc.     

基于截断tau的copula模型选择及应用

本文引进了截断tau,计论了它与生存阿基米德Copula之间的关系,并在此基础上提出一种新的选择Copula模型的方法,实例分析表明,这种构建Copula模型的方法较好反映了数据内在的信息,客观描述金融变量的相关性,便于尾部相关性分析,为金融市场相关性分析提供了一种新途径.     

Incorporating the information from direct and indirect neighbors into fitness evaluation enhances the cooperation in the social dilemmas

We propose an improved fitness evaluation method to investigate the evolution of cooperation in the spatial social dilemmas. In our model, a focal player’s fitness is calculated as the linear combination of his own payoff, the average payoffs of direct and indirect neighbors in which two independent selection parameters (α and β) are used to control the proportion of various payoff contribution to the current fitness. Then, the fitness-based strategy update rule is still Fermi-like, and asynchronous update is adopted here. A large plethora of numerical simulations are performed to validate the behaviors of the current model, and the results unambiguously demonstrate that the cooperation level is greatly enhanced by introducing the payoffs from the surrounding players. In particular, the influence of direct neighbors become more evident when compared with indirect neighbors since the correlation between focal players and their direct neighbors is much closer. Current outcomes are significant for us to further illustrate the origin and emergence of cooperation within a wide variety of natural and man-made systems.