关于对可转换债券融资发行范围的确定研究

可转换债券的双重属性以及企业生产经营状况使可转换债券融资具有很大的不确定性,最终导致不同的结果,结果的不同将影响企业进行融资决策中资本结构的制定,因而需要对可转换债券发行范围进行确定。完全转股和回售是企业经营状况良好和极差两种极端情况下可转债融资所产生的结果,此两种结果决定了可转换债券发行范围的上下限,将完全转股结果下所求得的最优融资资本结构作为可转债发行范围的上限,回售情况下得到的最优融资资本结构作为发行下限,从而得到可转换债券发行范围。最后利用基于随机模拟的遗传算法对融资决策模型进行求解,并通过算例解释了算法的有效性。     

基因技术的社会风险

基因技术给人类的生存与发展提供了许多希望。但是,基因技术对人类健康和生态环境具有潜在危害,对社会稳定和社会安全产生威胁,对人类价值观产生冲击,从而在社会各个层面产生意想不到的后果。因此,探讨基因技术将给人类带来的社会风险,提前采取预防措施,做到“未雨绸缪,防患于未然”,有助于促进基因技术的良性发展。     

Genetic programming as a means for programming computers by natural selection

Many seemingly different problems in machine learning, artificial intelligence, and symbolic processing can be viewed as requiring the discovery of a computer program that produces some desired output for particular inputs. When viewed in this way, the process of solving these problems becomes equivalent to searching a space of possible computer programs for a highly fit individual computer program. The recently developed genetic programming paradigm described herein provides a way to search the space of possible computer programs for a highly fit individual computer program to solve (or approximately solve) a surprising variety of different problems from different fields. In genetic programming, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (sexual recombination) operator appropriate for genetically mating computer programs. Genetic programming is illustrated via an example of machine learning of the Boolean 11-multiplexer function and symbolic regression of the econometric exchange equation from noisy empirical data.Hierarchical automatic function definition enables genetic programming to define potentially useful functions automatically and dynamically during a run, much as a human programmer writing a complex computer program creates subroutines (procedures, functions) to perform groups of steps which must be performed with different instantiations of the dummy variables (formal parameters) in more than one place in the main program. Hierarchical automatic function definition is illustrated via the machine learning of the Boolean 11-parity function.     

A likelihood ratio test for goodness‐of‐fit of recessive and dominant models for case–control studies

Testing goodness‐of‐fit of commonly used genetic models is of critical importance in many applications including association studies and testing for departure from Hardy–Weinberg equilibrium. Case–control design has become widely used in population genetics and genetic epidemiology, thus it is of interest to develop powerful goodness‐of‐fit tests for genetic models using case–control data. This paper develops a likelihood ratio test (LRT) for testing recessive and dominant models for case–control studies. The LRT statistic has a closed‐form formula with a simple $\chi^{2}(1)$ null asymptotic distribution, thus its implementation is easy even for genome‐wide association studies. Moreover, it has the same power and optimality as when the disease prevalence is known in the population. The Canadian Journal of Statistics 41: 341–352; 2013 © 2013 Statistical Society of Canada     

Fitting of self-exciting threshold autoregressive moving average nonlinear time-series model through genetic algorithm and development of out-of-sample forecasts

Self-exciting threshold autoregressive moving average (SETARMA) nonlinear time-series model is considered here. Sufficient conditions for invertibility and stationarity are derived. Parameter estimation algorithm is developed by employing real-coded genetic algorithm stochastic optimization procedure. A significant feature of the work done is that optimal out-of-sample forecasts up to three-step ahead and their forecast error variances are derived analytically. Relevant computer programs are written in statistical analysis system (SAS) and C. As an illustration, annual mackerel catch time-series data are considered. Forecast performance of the fitted model for hold-out data is evaluated by using Naive and Monte Carlo approaches. It is found that optimal out-of-sample forecast values are quite close to actual values and estimated variances are quite close to theoretical values. Superiority of the SETARMA model over the SETAR model for equal predictive ability through Diebold–Mariano test is also established.     

TSP及其扩展问题的混合型启发式算法

就经典的旅行商问题（ＴＳＰ）及其扩展形式;瓶颈问题、多目标问题等给出一种混合型启发式算法,并知微机上予以实现,为困难的扩展型ＴＳＰ提供了新的求解手段。     

一类两层规划问题模糊满意解的遗传算法

在现有两层规划问题求解方法的基础上,提出用浮点数编码的遗传算法求解该问题模糊满意解的新方法．这种方法每次提供给决策者一组近似最优解,通过决策者的比较、评价和选择,在交互过程中得到各决策者都满意的解．该方法不仅可以给决策者提供更多的决策环境信息,而且可以适应决策者偏好的变化,使得决策过程更合理,更符合人的认识过程．     

生物遗传资源的国际保护

生物遗传资源是生物技术发展的重要战略资源,拥有先进生物技术的发达国家和拥有丰富遗传资源的发展中国家之间存在着巨大的利益冲突,为了协调双方的利益,公平合理地保护和利用遗传资源的国内和国际立法是必需的。作者通过对农民权理论、生物遗传资源永久主权理论以及国内主要观点的分析和评价,提出了保护生物遗传资源的理论基础———生物遗传资源权。     

应用混合神经网络和遗传算法的期权价格预测模型

隐含波动率是指在市场中观察的期权价格所蕴涵的波动率.提出了一种加权的隐含波动率作为混合神经网络的输入变量,建立了混合神经网络和遗传算法相结合的期权价格预测模型,通过遗传算法来优化神经网络的结构和获得隐含波动率的权重.在对香港金融衍生品市场的实证中表明,本文模型在预测结果上要优于传统的Black-Scholes模型.     

A Bayesian hierarchical approach to dual response surface modelling

In modern quality engineering, dual response surface methodology is a powerful tool to model an industrial process by using both the mean and the standard deviation of the measurements as the responses. The least squares method in regression is often used to estimate the coefficients in the mean and standard deviation models, and various decision criteria are proposed by researchers to find the optimal conditions. Based on the inherent hierarchical structure of the dual response problems, we propose a Bayesian hierarchical approach to model dual response surfaces. Such an approach is compared with two frequentist least squares methods by using two real data sets and simulated data.