Learning of cycles and sunspot equilibria by Genetic Algorithms

We analyze the learning behaviour of a Simple Genetic Algorithm in an overlapping generations model with one consumption good and fiat money. It is shown by simulations, that in cases where periodic equilibria exist the equilibrium of period two is learned by a Genetic Algorithm and not the monetary steady state. We further show that proper coding leads to convergence of the GA towards the sunspot equilibrium. If individuals who believe in the impact of sunspots are brought together with individuals who ignore the sunspots, the sunspot believes will in most cases drive the other individuals out of the population.     

Joint pricing and design of urban highways with spatial and user group heterogeneity

This paper addresses the joint optimization of capacity investments and toll charges imposed on multi-group users in monopolistic private highways within general road networks. A game-theoretic formulation is provided that leads to a nonconvex bilevel program. The proposed modeling framework handles several complex issues raised in realistic applications, such as regulations on the levels of tolls and service, and the discrete nature of highway capacity, using a genetic optimization technique. Real-application results show the importance of considering the spatial heterogeneity of prices, and the tradeoff between investments and pricing strategies in regulated private highways.     

The joint replenishment and delivery scheduling of the one-warehouse, n-retailer system

We deal with the joint replenishment and delivery scheduling of the one-warehouse, n-retailer system in this paper. We suggest a more flexible policy for the joint replenishment and delivery scheduling of a warehouse compared with the existing researches. We introduce the mathematical model and two efficient algorithms for the joint replenishment and delivery scheduling of the warehouse. Subsequently, we develop the hybrid genetic algorithm (GA) and compare it with two efficient heuristic algorithms for extensive computational experiments. Further, we show the advantages of our GA in dealing easily with resource restrictions.     

基于遗传算法的质量成本优化及控制研究

针对建筑工程质量形成的过程,本根据系统可靠度原理,并应用遗传算法进行质量成本的优化,使建筑工程中的可靠性与经济性很好的协调起来,为建筑工程领域及其他领域的质量成本的优化及控制提供了一个新的思路。     

各种指数基金模型的实证比较分析

在指数基金管理方面,存在着多种优化的指数投资组合的构建方法和绩效评估方法,本文以实证研究的形式,比较分析了由5种“选股”方法和5种“资金配置”方法所构成的25种投资组合在各种绩效评估方法下的表现。最终得出结论认为,在遗传算法下利用DMinMax模型配置资产权重将具有较好的投资绩效。     

建筑物结构损伤诊断技术现状与展望

本文介绍了结构损伤诊断技术在土木工程中的重要性,综述了结构损伤诊断的研究现状,特别时结构损伤智能诊断方法做了介绍。最后,展望了结构损伤诊断的研究和发展方向。     

Testing assumptions underlying economic research on transgenic food crops for Third World farmers: Evidence from Cuba, Guatemala and Mexico

Transgenic crop varieties (TGVs) are being promoted as essential for improving small-scale Third World (SSTW) agriculture. Most economic research on this topic makes critical, untested assumptions, including that farmers will choose TGVs over other varieties because TGVs are economically optimal and because farmers are risk neutral profit maximizers. We tested these assumptions using data from a survey of 334 farmers in 6 communities in Cuba, Guatemala and Mexico in which farmers ranked 4 real and hypothetical maize varieties for eating and sowing. Our results did not support these assumptions. Most farmers preferred farmer varieties for sowing and especially for eating, avoiding TGVs, a preference associated with being risk averse and with non-monetary preferences. Farmers more integrated into modern agriculture were more likely to choose TGVs. These results suggest that farmers most in need of support and most important for conserving genetic diversity are least favorable toward TGVs, and that alternative ways of improving SSTW agriculture should receive more attention.     

遗传算法及其在客户关系管理中的应用研究

遗传算法是一种宏观意义下的仿生优化算法，它模拟生物在自然环境中的遗传和进化过程而形成的一种自适应全局优化概率搜索算法。这种算法可以用于对海量的客户信息进行搜索，提取对决策有用的信息，让企业在激烈的市场竞争中处于有利地位，最终达到最高利润。本文还通过一个简单的实例研究将该算法应用在客户关系管理中。