A hybrid genetic algorithm for the batch sequencing problem on identical parallel machines

The batch sequencing problem (BSP) has been the focus of much research thanks to its practical applicability and NP-hard nature. This study aims to solve an extension of the problem from single machine case to parallel machine case. A dispatching rule called the EDD (Earliest Due Date)-Greedy heuristic and a pure genetic algorithm (GA) are proposed, and then combine them through an insertion method called the Gene Bank Strategy. Several test cases of up to 12 machines and 60 jobs are randomly generated and solved. Those experiments show that the introduced hybrid GA can provide far better results than its two component algorithms that can also independently solve the problem.     

Nonparametric estimation of the shape function in a Gamma process for degradation data

The author considers estimation under a Gamma process model for degradation data. The setting for degradation data is one in which n independent units, each with a Gamma process with a common shape function and scale parameter, are observed at several possibly different times. Covariates can be incorporated into the model by taking the scale parameter as a function of the covariates. The author proposes using the maximum pseudo‐likelihood method to estimate the unknown parameters. The method requires usage of the Pool Adjacent Violators Algorithm. Asymptotic properties, including consistency, convergence rate and asymptotic distribution, are established. Simulation studies are conducted to validate the method and its application is illustrated by using bridge beams data and carbon‐film resistors data. The Canadian Journal of Statistics 37: 102‐118; 2009 © 2009 Statistical Society of Canada     

Maximizing a Family of Optimal Statistics over a Nuisance Parameter with Applications to Genetic Data Analysis

In this article, a simple algorithm is used to maximize a family of optimal statistics for hypothesis testing with a nuisance parameter not defined under the null hypothesis. This arises from genetic linkage and association studies and other hypothesis testing problems. The maximum of optimal statistics over the nuisance parameter space can be used as a robust test in this situation. Here, we use the maximum and minimum statistics to examine the sensitivity of testing results with respect to the unknown nuisance parameter. Examples from genetic linkage analysis using affected sub pairs and a candidate-gene association study in case-parents trio design are studied.     

Genetic algorithms for numerical optimization

Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian striving for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where no algorithm is known. However, such applications can encounter problems that sometimes delay, if not prevent, finding the optimal solutions with desired precision. In this paper we describe applications of GAs to numerical optimization, present three novel ways to handle such problems, and give some experimental results.     

蛋白质谱数据的特征提取

变性蛋白质与正常对照组的蛋白质的HPLC-MS谱图存在人工无法发现的差异性,也是本文所说的特征,本文的研究工作就是通过信息学的方法寻找这些特征。实际工作中,我们先从谱图中提取数据,重新采样,并且对采样数据进行基线校正,归一化,噪声降解,得到预处理好的数据集。然后利用遗传算法来最优化结果,找到了变性蛋白质的特征谱线。我们期望根据这些特征建立未知蛋白质的分类模型,为疾病的诊断提供依据。     

基于模拟退火的进化算法性能对比研究

将模拟退火算法和遗传算法、粒子群优化算法分别进行结合,形成模拟退火—遗传算法以及模拟退火—粒子群优化算法,并作性能对比分析。研究结果表明,这两种算法都在进化代数和全局寻优能力方面有较大突破,在找寻最佳个体解的效率上,模拟退火—粒子群优化算法更突出。     

一维优化的最优步长因子法

本文在分析一维优化方法的比例因子法的基础上,提出了一种直接调整步长的最优步长因子法并给出框图。该法较比例因子法理论上完善,迭代控制可靠。     

A genetic algorithm tutorial

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.     

A genetic algorithm for determining optimal replenishment cycles to minimize maximum warehouse space requirements

In a supply chain, it is an important issue for logistic managers to offset the replenishment cycles of multiple products sharing a warehouse so as to minimize the maximum warehouse space requirement (MWSR). Most of the studies in the literature assume that warehouses replenish at the beginning of some basic planning period. In this paper, we relax this assumption by allowing the warehouse to replenish at any time. In order to solve this problem, we conduct theoretical analysis based on Fourier series and Fourier transforms and propose a procedure that is used to calculate MWSR efficiently for any given replenishment schedule. Then, we employ this procedure in a genetic algorithm (GA) to search for the optimal replenishment schedule. Using randomly generated instances, we show that the proposed GA significantly outperforms a previously published heuristic.     

矿井掘进工作面安全评价的灰色聚类方法

本文用灰色聚类方法[1]讨论了煤矿掘进工作面安全评价问题,提出了一种新的安全评价方法─灰色聚类综合法。同时,本文对灰色聚类的算法作了改进,改进后的算法便于记忆和应用。