遗传算法的改进与实现

本文在对遗传算法基本思想进行介绍的基础上,对具体的操作过程进行了改进,并给出了具体程序设计方法。     

一个水流问题的遗传算法求解

应用遗传算法(GA)来讨论一个水流问题.这个水流问题曾是不少统计学者用来考察不同试验设计和建模方法的常用案例.通过本例旨在说明遗传算法确为求解复杂系统优化问题的有力工具.     

Genetic algorithms in statistical tolerancing

An important aspect in manufacturing design is the distribution of geometrical tolerances so that an assembly functions with given probability, while minimising the manufacturing cost. This requires a complex search over a multidimensional domain, much of which leads to infeasible solutions and which can have many local minima. As well, Monte-Carlo methods are often required to determine the probability that the assembly functions as designed. This paper describes a genetic algorithm for carrying out this search and successfully applies it to two specific mechanical designs, enabling comparisons of a new statistical tolerancing design method with existing methods.     

Optimal Product Line Design: Genetic Algorithm Approach to Mitigate Cannibalization

In this marketing-oriented era where manufacturers maximize profits through customer satisfaction, there is an increasing need to design a product line rather than a single product. By offering a product line, the manufacturer can customize his or her products to the needs of a variety of segments in order to maximize profits by satisfying more customers than a single product would. When the amount of data on customer preferences or possible product configurations is large and no analytical relations can be established, the problem of an optimal product line design becomes very difficult and there are no traditional methods to solve it. In this paper, we show that the usage of genetic algorithms, a mathematical heuristics mimicking the process of biological evolution, can solve efficiently the problem. Special domain operators were developed to help the genetic algorithm mitigate cannibalization and enhance the algorithm’s local search abilities. Using manufacturer’s profits as the criteria for fitness in evaluating chromosomes, the usage of domain specific operators was found to be highly beneficial with better final results. Also, we have hybridized the genetic algorithm with a linear programming postprocessing step to fine tune the prices of products in the product line. Attacking the core difficulty of cannibalization in the algorithm, the operators introduced in this work are unique.     

Theoretical and Experimental Study of Crossover Operators of Genetic Algorithms

This paper is concerned with crossover operators for genetic algorithms (GAs) which are used to solve problems based on real numbers. First, a classification of the operators is introduced, dividing crossover into a vector-level and a variable-level operator. The theoretical study of variable-level operators for binary coded GAs leads to the discovery of two properties, which are used to define certain characteristics of crossover operators used by real-number encoded GAs. For variable-level operators, the experimental distributions of the offspring variables of given pairs of parent variables are then found. Finally, an experimental comparison of crossover operator performance is carried out.     

遗传算法基础理论研究的新近发展

本文综述了有关遗传算法收敛性及收敛速度估计的近期研究结果,在分类概述相关的Ｖｏｓｅ－Ｌｉｅｐｉｎｓ模型、Ｍａｒｋｏｖ链模型、公理化模型、连续（积分算子）模型及收敛速度估计、迭代次数估计与时间复杂性估计的基础上,指出遗传算法理论研究存在的其它亟待解决的问题。     

模糊择近原则在多目标容差设计中的应用

利用模糊数学中的择近原则,以容差-成本模型为基础,通过引入模糊数学中的隶属度函数和贴近度原则,结合实验并设计方法提出了一种模糊容差稳健优化设计方法,较好地解决了多目标容差设计的全局最优问题.最后通过一个实例验证了该方法的合理、有效性.     

考虑服务特性的复杂产品质量容差设计研究

由于计算复杂性,传统稳健设计方法难以有效解决复杂产品质量特性容差设计问题。而随着服务型制造的兴起,如何对复杂产品中包含的服务特性进行容差设计成为尚待解决的难题。本文首先在CCD优化试验设计基础上,运用置信域方法结合蒙特卡洛仿真方法解决复杂产品硬件质量容差设计问题,然后运用质量损失函数对复杂产品服务质量容差设计进行探究,最后通过算例验证了方法的有效性,以期为解决该类问题提供有益探索。     

应用稳健性设计改进真空镀膜过程质量

本文研究了M公司对某型号手机外框表面进行真空镀膜,该产品交付客户后出现黑点或黑点群的严重质量问题。在找出该问题的质量关键特性基础上,利用稳健性参数设计,采用的部分因子设计作为控制表以及的噪声表组成的乘积表进行试验找到了影响本问题中产品喷漆厚度的散度因子和调节因子,并找出各参数的优化值并进行验证,从而成功改进该质量问题。     

遗传算法与神经网络相结合的降水预报模型

针对目前BP神经网络在实际应用中,网络结构难以确定以及网络极易陷入局部解问题,用遗传算法优化神经网络的连接权和网络结构,在遗传进化过程中采取保留最佳个体的方法,建立基于遗传算法的BP网络模型,同时通过实例说明该模型在降水预测中的应用,计算结果表明该方法的预测精度较高。     

基于遗传算法的物流配送车辆调度问题研究

研究使用遗传算法求解物流配送组织过程中车辆调度问题 .通过把时间窗约束和车辆容量约束转嫁到最小费用目标函数中去 ,建立适合于遗传算法的车辆调度模型 .阐述放回式随机复制算子和适应度函数 ,设计描述行驶线路的染色体结构、初始群体生成方法、独特的交叉算子和交换变异算子 ,构造完整的遗传算法 .并给出算例 ,验证调度模型和遗传算法 .     

Performance Analysis of Adaptive Genetic Algorithms with Fuzzy Logic and Heuristics

In this paper, we propose some genetic algorithms with adaptive abilities and compare with them. Crossover and mutation operators of genetic algorithms are used for constructing the adaptive abilities. All together four adaptive genetic algorithms are suggested: one uses a fuzzy logic controller improved in this paper and others employ several heuristics used in conventional studies. These algorithms can regulate the rates of crossover and mutation operators during their search process. All the algorithms are tested and analyzed in numerical examples. Finally, a best genetic algorithm is recommended.     

基于遗传算法的附随扩散模型的构建及实证研究

论文研究了具有附随扩散关系的产品扩散特点,以Bass模型为理论基础,构建了附随扩散模型,然后以移动上网用户附随移动用户扩散的实例为研究对象,建立了移动上网附随扩散模型,并采用遗传算法估算模型参数,对今后几年移动上网用户的扩散规律进行了预测。最后,将附随扩散模型的研究结果与采用传统Bass模型、Logistic模聊的结果进行了对比,得出了附随扩散模型拟合和预测效果更好的结论。     

Optimized Crossover-Based Genetic Algorithms for the Maximum Cardinality and Maximum Weight Clique Problems

In Balas and Niehaus (1996), we have developed a heuristic for generating large cliques in an arbitrary graph, by repeatedly taking two cliques and finding a maximum clique in the subgraph induced by the union of their vertex sets, an operation executable in polynomial time through bipartite matching in the complement of the subgraph. Aggarwal, Orlin and Tai (1997) recognized that the latter operation can be embedded into the framework of a genetic algorithm as an optimized crossover operation. Inspired by their approach, we examine variations of each element of the genetic algorithm—selection, population replacement and mutation—and develop a steady-state genetic algorithm that performs better than its competitors on most problems.     

基于遗传算法的岩体参数辨识研究

目前,对于岩体流固耦合分析研究已经很多,而耦合分析常常受困于计算参数的取值,因此对两场耦合模型中的计算参数反演分析是非常必要的.根据实测的水头、位移资料,利用遗传算法,建立了等效连续岩体渗流场与应力场耦合计算参数辨识模型.并对某算例在库水位下降情况下,以渗流场与应力场耦合正分析计算结果作为"实测值",进行两场耦合参数辨识分析.从参数辨识的结果来看,验证了所提出的思路、方法以及程序的正确性和可行性.两场耦合计算参数进行反演分.析,对于两场耦合模型的建立和计算结果的可靠性是非常有意义的.     

遗传算法BP神经网络的预报研究和应用

针对目前 BP神经网络在实际气象预报应用中 ,网络结构难以确定以及网络极易陷入局部解问题 ,用遗传算法优化神经网络的连接权和网络结构 ,并在遗传进化过程中采取保留最佳个体的方法 ,建立基于遗传算法的 BP网络模型 ,并以广西的月降水量进行实例分析 ,计算结果表明 ,该方法预报精度高、而且稳定 .     

Using genetic algorithm for dynamic and multiple criteria web-site optimizations

In today’s competitive electronic marketplace, companies try to create long-lasting relations with their online customers. Log files and registration forms generate millions of online transactions. Companies use new techniques to “mine” these data and establish optimal online storefronts to maximize their web presence. Several criteria, such as minimization of download time, maximization of web-site visualization and product association level, can be used for the optimization of virtual storefronts. This paper introduces a genetic algorithm, to be used in a model-driven decision-support system for web-site optimizations. The algorithm ensures multiple criteria web-site optimizations, and the genetic search provides dynamic and timely solutions independent of the number of objects to be arranged.     

Extended Fuzzy C-Means and Genetic Algorithms to Optimize Power Flow Management in Hybrid Electric Vehicles

The need for personal transportation must be harmonized by considering the impact of so huge number of vehicles on the environment. The adoption of hybrid electric vehicles can provide a sensible improvement from an environmental viewpoint, but at the same time makes more difficult the definition and implementation of the overall powertrain control mechanism. In fact, powertrain control problems are known to be very complex due to conflicting requirements, and this difficulty augments in case of hybrid electric vehicles. Most of the features of the future hybrid electric vehicles are enabled by a new energy flow management unit designed to split the instantaneous power demand between the internal combustion engine and the electric motor, ensuring both an efficient power supply and reduced emissions. Classic approaches that rely on static thresholds, optimized on a fixed drive cycle, cannot face the high dynamicity and unpredictability of real-life drive conditions. The need to actually control a real vehicle stimulates the research of innovative methodologies for the real-time identification of the operating points of each energy source. This paper is framed into this context: after a brief discussion about a non-conventional formalization of the energy flows problem based on a multiobjective function, a knowledge-based control system for splitting the vehicle's power demand between the engine and motor is presented. The proposed approach exploits a fuzzy clustering criterion that combined with a genetic algorithm, permits to achieve better results, both in terms of a reduced computational effort and an improved efficiency of the control system over various driving cycles. To validate the proposed approach, simulation tests and comparisons with other energy management strategies are discussed.     

A model and solution method for multi-period sales promotion design

This research addresses the optimal design of a series of promotions (which might offer free gifts, discounts, or special services) periodically mailed to potential customers. A model and methodology are presented which maximize the multiple purchases of these customers over time using opinions from both promotion designers and customers. A Genetic Algorithm-based heuristic is developed to efficiently arrive at good promotion designs, and the methodology is applied to a problem using real data.     

三维装箱问题的模型与改进遗传算法

三维装箱问题是一类NP-hard的组合优化问题,构建一个适当的数学模型并设计高效快速的算法具有重要的理论和现实意义.该文将箱子空间划分为立方体单元,依此构建三维装箱问题的混合整数规划模型,并通过改进遗传算法求解,剔除大量不可行解提高了收敛速度.实验结果表明此算法运算过程及结果稳定,具有较强的实际应用价值,能有效解决复杂的三维装箱问题.