带残损原材料的二维优化下料问题的研究

本文将遗传算法和模拟退火算法相结合来求解二雏优化下料问题，下料件为不规则形状，原材料上带有残损，问题具有一般性。并在Windows2000平台下，用Matlab实现上述算法。通过实例验证了该算法的有效性。     

一种减轻显式组播路由节点负载的策略

本文基于显式组播（Explicit Multicast）机制和遗传算法的思想，提出了一种通过减少组播组中存放转发信息的路由节点数量来减轻路由路径负担的策略。通过算法描述和分析及实验仿真，此方案可以有效地节省网络资源。     

通过缓冲高适应度个体改进遗传算法实现的收敛特性

文中概要阐述了遗传算法的算法实现产生过早收敛的原因，提出了在常规遗传算法中增加对最优个体缓冲的改进算法，延长具有高适应度个体的生存期，进而维持较高的种群多样性，以获得更好的收敛速度和优化解，并对建立缓冲区的一些规则进行了讨论。文中以求解旅行商问题为例，对改进前后的遗传算法的运行情况进行对比分析，结论为改进算法能获得更好的收敛性能。     

Many-to-many location-routing with inter-hub transport and multi-commodity pickup-and-delivery

In this paper, we consider a variant of the many-to-many location-routing problem, where hub facilities have to be located and customers with either pickup or delivery demands have to be combined in vehicle routes. In addition, several commodities and inter-hub transport processes are taken into account. A practical application of the problem can be found in the timber-trade industry, where companies provide their services using hub-and-spoke networks. We present a mixed-integer linear model for the problem and use CPLEX 12.4 to solve small-scale instances. Furthermore, a multi-start procedure based on a fix-and-optimize scheme and a genetic algorithm are introduced that efficiently construct promising solutions for medium- and large-scale instances. A computational performance analysis shows that the presented methods are suitable for practical application.     

A bi-objective mathematical model toward dynamic cell formation considering labor utilization

This paper addresses dynamic cell formation problem (DCFP) via a new bi-objective mathematical formulation. Although literature body of DCFP includes a vast number of research instances, human-related issues are mostly neglected as an important aspect of DCFP in the corresponding literature. In this paper, the first objective function seeks to minimize relevant costs of the problem including machine procurement and relocation costs, machine variable cost, inter-cell movement and intra-cell movement costs, overtime cost and labor shifting cost between cells, while labor utilization of the modeled DCFP is maximized through the second objective function. Due to NP-hardness of DCFP, an ant colony optimization (ACO) meta-heuristic is developed for the first time in the literature to tackle the problem. Also, authors enhance diversification of the developed algorithm is enhanced by hybridization of the proposed ACO algorithm with a genetic one. Finally, some numerical samples are generated randomly to validate the proposed model by which strengths of the developed algorithm is shown.     

Locally Optimized Crossover for the Traveling Umpire Problem

This paper presents a genetic algorithm (GA) to solve the Traveling Umpire Problem, which is a recently introduced sports scheduling problem that is based on the most important features of the real Major League Baseball umpire scheduling problem. In our GA, contrary to the traditional way of randomly obtaining new solutions from parent solutions, we obtain partially optimized solutions with a Locally Optimized Crossover operator. This operator also presents a link between the evolutionary mechanism on a population of solutions and the local search on a single solution. We present improved results over other methods on benchmark instances.     

The lifetime of CFC substitutes studied by a network trained with chaotic mapping modified genetic algorithm and DFT calculations

The hydrohaloalkanes have attracted much attention as potential substitutes of chlorofluorocarbons (CFCs) that deplete the ozone layer and lead to great high global warming. Having a short atmospheric lifetime is very important for the potential substitutes that may also induce ozone depletion and yield high global warming gases to be put in use. Quantitative structure–activity relationship (QSAR) studies were presented for their lifetimes aided by the quantum chemistry parameters including net charges, Mulliken overlaps, E _HOMO and E _LUMO based on the density functional theory (DFT) at B3PW91 level, and the C-H bond dissociation energy based on AM1 calculations. Outstanding features of the logistic mapping, a simple chaotic system, especially the inherent ability to search the space of interest exhaustively have been utilized. The chaotic mapping aided genetic algorithm artificial neural network training scheme (CGANN) showed better performance than the conventional genetic algorithm ANN training when the structure of the data set was not favorable. The lifetimes of HFCs and HCs appeared to be greatly dependent on their energies of the highest occupied molecular orbitals. The perference of the RMSRE comparing to RMSE as objective function of ANN training was better for the samples of interest with relatively short lifetimes. C₂H₆ and C₃H₈ as potential green substitutes of CFCs present relatively short lifetimes.     

Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data

Cost-sensitive classification is based on a set of weights defining the expected cost of misclassifying an object. In this paper, a Genetic Fuzzy Classifier, which is able to extract fuzzy rules from interval or fuzzy valued data, is extended to this type of classification. This extension consists in enclosing the estimation of the expected misclassification risk of a classifier, when assessed on low quality data, in an interval or a fuzzy number. A cooperative-competitive genetic algorithm searches for the knowledge base whose fitness is primal with respect to a precedence relation between the values of this interval or fuzzy valued risk. In addition to this, the numerical estimation of this risk depends on the entrywise product of cost and confusion matrices. These have been, in turn, generalized to vague data. The flexible assignment of values to the cost function is also tackled, owing to the fact that the use of linguistic terms in the definition of the misclassification cost is allowed.     

Induction of decision trees using genetic programming for modelling ecotoxicity data: adaptive discretization of real-valued endpoints

Recent literature has demonstrated the applicability of genetic programming to induction of decision trees for modelling toxicity endpoints. Compared with other decision tree induction techniques that are based upon recursive partitioning employing greedy searches to choose the best splitting attribute and value at each node that will necessarily miss regions of the search space, the genetic programming based approach can overcome the problem. However, the method still requires the discretization of the often continuous-valued toxicity endpoints prior to the tree induction. A novel extension of this method, YAdapt, is introduced in this work which models the original continuous endpoint by adaptively finding suitable ranges to describe the endpoints during the tree induction process, removing the need for discretization prior to tree induction and allowing the ordinal nature of the endpoint to be taken into account in the models built.