A solution method for optimal weight design problem of the gear using genetic algorithms

In this paper, we formulate an optimal weight designproblem of a gear for a constrained bending strength of gear, tortional strength of shafts and each gear dimension as a nonlinear integer programming (NIP) problem and solve it directly by keeping the nonlinear constraint by using an improved genetic algorithm (GA). We discuss the efficency of the proposed method.     

A FORTRAN 90 genetic algorithm module for composite laminate structure design

The design of the stacking sequence for a composite laminate involves a set of discrete variables (plymaterial and ply orientation), and is thus well-suited to genetic algorithms for design optimization. Such algorithms have typically been custom-designed in FORTRAN 77 to suit specific optimization problems. Fortran 90 is a modern, powerful language with features that support important programming concepts, including those used in object-oriented programming. The Fortran 90 genetic algorithm module is used to define genetic data types, the functions which use these data types, and to provide a general framework for solving composite laminate structure design problems. The language's support of abstract data types is used to build genetic structures such as populations, subpopulations, individuals, chromosomes, and genes, and these data types are combined and manipulated by module subroutines. The use of abstract data types and long variable names makes the code useful and easily understood, while dynamic memory allocation makes the module flexible enough to be used in design problems of varying size and specification.     

Modular technical change and genetic algorithms

Given knowledge is distributed across the economic population, it is appropriate to consider technical change as a process of distributed learning. This leads naturally to an evolutionary perspective. Noting the work of cognitive sciences, which uses a computational model of the mind, we are drawn to models based on genetic algorithms (GAs). Using the concept of modular technologies we are able to offer an interpretation of the GA as a model of population learning. A model involving the coevolution of implemented technologies and technological models in introduced; a highly simplified version of the model is used to assess the use of the GA approach, particularly Arifovic's augmented version.     

量子遗传算法在TSP中的应用

本文提出了一种改进的量子遗传算法,其核心是对量子遗传算法中的量子旋转门的调整策略进行改进。在现有的静态、指数型动态调整策略的基础上提出了基于正弦函数的动态调整策略。文中对旅行商问题（TSP）的仿真实验结果表明：改进后的算法的优化质量和效率都优于遗传算法和一般量子遗传算法。     

Order acceptance using genetic algorithms

This paper uses a genetic algorithm to solve the order-acceptance problem with tardiness penalties. We compare the performance of a myopic heuristic and a genetic algorithm, both of which do job acceptance and sequencing, using an upper bound based on an assignment relaxation. We conduct a pilot study, in which we determine the best settings for diversity operators (clone removal, mutation, immigration, population size) in connection with different types of local search. Using a probabilistic local search provides results that are almost as good as exhaustive local search, with much shorter processing times. Our main computational study shows that the genetic algorithm always dominates the myopic heuristic in terms of objective function, at the cost of increased processing time. We expect that our results will provide insights for the future application of genetic algorithms to scheduling problems.     

Machine-component grouping using genetic algorithms

One major problem in cellular manufacturing is the grouping of component parts with similar processing requirements into part families, and machines into manufacturing cells to facilitate the manufacturing of specific part families assigned to them. The objective is to minimize the total inter-cell and intra-cell movements of parts during the manufacturing process. In this paper, a mathematical model is presented to describe the characteristics of such a problem. An approach based on the concept of genetic algorithms is developed to determine the optimal machine-component groupings. Illustrative examples are used to demonstrate the efficiency of the proposed approach. Indeed, the results obtained show that the proposed genetic approach is a simple and efficient means for solving the machine-component grouping problem.     

Motion fairing using genetic algorithms

In this paper, we solve the motion smoothing problem using genetic algorithms. Smooth motion generation is essential in the computer animation and virtual reality area. The motion of a rigid body in general consists of translation and orientation. The former is described by a space curve in three-dimensional Euclidean space while the latter is represented by a curve in the unit quaternion space. By adopting the geometric approach, the smoothness of both translation data and orientation data is measured from the strain energy perspective and a nonlinear optimization problem is formulated that aims to minimize the weighted sum of the strain-energy and the sum of the squared errors. A hybrid algorithm that combines genetic algorithms and local search schemes is deployed to solve this optimization problem and the experiments show that both smoothness and shape preservation of the resulting motion can be achieved by the proposed algorithm.     

Two-dimensional packing problems using genetic algorithms

This paper presents a technique for applying genetic algorithms for the two-dimensional packing problem. The approach is applicable to not only convex shaped objects, but can also accommodate any type of concave and complex shaped objects including objects with holes. In this approach, a new concept of a two-dimensional genetic chromosome is introduced. The total layout space is divided into a finite number of cells for mapping it into this 2D genetic algorithm chromosome. The mutation and crossover operators have been modified and are applied in conjunction with connectivity analysis for the objects to reduce the creation of faulty generations. A new feature has been added to the Genetic Algorithm (GA) in the form of a new operator called compaction. Several examples of GA-based layout are presented.     

Dynamic vehicle routing using genetic algorithms

Many difficult combinatorial optimization problems have been modeled as static problems. However, in practice, many problems are dynamic and changing, while some decisions have to be made before all the design data are known. For example, in the Dynamic Vehicle Routing Problem (DVRP), new customer orders appear over time, and new routes must be reconfigured while executing the current solution. Montemanni et al. [1] considered a DVRP as an extension to the standard vehicle routing problem (VRP) by decomposing a DVRP as a sequence of static VRPs, and then solving them with an ant colony system (ACS) algorithm. This paper presents a genetic algorithm (GA) methodology for providing solutions for the DVRP model employed in [1]. The effectiveness of the proposed GA is evaluated using a set of benchmarks found in the literature. Compared with a tabu search approach implemented herein and the aforementioned ACS, the proposed GA methodology performs better in minimizing travel costs. Franklin T. Hanshar is currently a M.Sc. student in the Department of Computing and Information Science at the University of Guelph, Ontario, Canada. He received a B.Sc. degree in Computer Science from Brock University in 2005. His research interests include uncertain reasoning, optimization and evolutionary computation. Beatrice Ombuki-Berman is currently an Associate Professor in the Department of Computer Science at Brock University, Ontario, Canada. She obtained a PhD and ME in Information Engineering from University of The Ryukyus, Okinawa, Japan in 2001 and 1998, respectively. She received a B.Sc. in Mathematics and Computer Science from Jomo Kenyatta University, Nairobi, Kenya. Her primary research interest is evolutionary computation and applied optimization. Other research interests include neural networks, machine learning and ant colony optimization.     

Efficient solutions for Mastermind using genetic algorithms

We present a new genetic algorithm for playing the game of Mastermind. The algorithm requires low run-times and results in a low expected number of guesses. Its performance is comparable to that of other meta-heuristics for the standard setting with four positions and six colors, while it outperforms the existing algorithms when more colors and positions are examined. The central idea underlying the algorithm is the creation of a large set of eligible guesses collected throughout the different generations of the genetic algorithm, the quality of each of which is subsequently determined based on a comparison with a selection of elements of the set.     

Design of laminated composite structures for optimum fiber direction and layer thickness,using optimality criteria

In this study, two optimality criteria are presented for optimum design of composite laminates using finite element method. Thickness of the layers and fiber orientation angles in each finite element are considered as the design variables. It will be shown that the optimum design of composite laminates with varying fiber orientations and layers thicknesses may be found by using these optimality criteria in an efficient way, without performing the sensitivity analysis.     

Adaptive reconfiguration of data networks using genetic algorithms

Genetic algorithms are applied to an important, but little investigated, network design problem, that of reconfiguring the topology and link capacities of an operational network to adapt to changes in its operating conditions. These conditions include: which nodes and links are unavailable; the traffic patterns; and the quality of service (QoS) requirements and priorities of different users and applications. Dynamic reconfiguration is possible in networks that contain links whose endpoints can be easily changed, such as satellite channels, terrestrial wireless connections, and certain types of optical connections. We report preliminary results that demonstrate the feasibility of performing genetic search quickly enough for online adaptation.     

基于遗传算法求任意函数的数值积分

针对复杂函数的数值积分问题,给出了若干个任意分割积分区间的数值积分的误差结果,并提出一种基于遗传算法的不等距节点分割的数值积分方法。该方法初始时在积分区间内任意选取一定的节点,通过遗传算法优化这些节点,在相邻节点间利用Simpson公式近似计算积分,最后得到较准确的积分结果。数值计算结果表明,该方法计算精度高,而且可以计算奇异函数及震荡函数的积分。     

Cost optimization of composite beams using genetic algorithms

This paper presents a genetic algorithm model for the cost optimization of composite beams based on the load and resistance factor design (LRFD) specifications of the AISC. The model formulation includes the cost of concrete, steel beam, and shear studs. Two design examples taken from the literature were analyzed in order to validate the proposed model, to illustrate its use, and to demonstrate its capabilities in optimizing composite beam designs. The results obtained show that the model is capable of achieving substantial cost savings. Hence, it can be of practical value to structural designers. A parametric study was also conducted to investigate the effects of beam spans and loadings on the cost optimization of composite beams.     

一种求解0-1背包问题的新遗传算法

众所周知,遗传算法的运行机理及特点是具有定向制导的随机搜索技术,其定向制导的原则是： 导向以高适应度模式为祖先的“家族”方向。以此结论为基础,利用随机化均匀设计的理论和方法,对遗传算法中的交叉操作进行了重新设计,给出了一个新算法,称之为随机化均匀设计遗传算法。最后将随机化均匀设计遗传算法应用于求解0-1背包问题,并与简单遗传算法和佳点集遗传算法进行比较。通过模拟比较,可以看出新的算法不但提高了算法的速度和精度,而且避免了其他方法常有的早期收敛现象。     

支持创新设计的多Agent系统

提出了一种支持创新设计的多Agent系统，该系统为设计人员提供有效的辅助工具。多Agent为设计人员的相互协作提供了支撑框架，基于树结构的遗传算法拓宽了设计人员的思维空间。以花瓶设计为例描述了系统的辅助设计过程。该实例利用VC自带的图形函数库OpenGL生成形状草图，用基于特征的产品设计树产生可选的部件组合，用遗传算法实现创新设计。     

Structural pattern recognition using genetic algorithms

This paper presents a genetic algorithm (GA) based optimization procedure for the solution of structural pattern recognition problem using the attributed relational graph representation and matching technique. In this study, candidate solutions are represented by integer strings and the population is randomly initialized. The GA is employed to generate a monomorphic mapping. As all the mapping constraints are not enforced during the search phase in order to speedup the search, an efficient pose clustering algorithm is used to eliminate spurious matches and to determine the presence of the model in the scene. The performance of the proposed approach to pattern recognition by subgraph isomorphism is demonstrated using line patterns and silhouette images.     

Fuzzy assembly line balancing using genetic algorithms

In this paper, we implement genetic algorithms to synthesis fuzzy assembly line balancing problem which is well-known as a NP-hard problem. The genetic operators concerned with the feasibility of chromosomes will be discussed, and its performance will be shown with a numerical example.     

Fuzzy classifier design using genetic algorithms

A new method for design of a fuzzy-rule-based classifier using genetic algorithms (GAs) is discussed. The optimal parameters of the fuzzy classifier including fuzzy membership functions and the size and structure of fuzzy rules are extracted from the training data using GAs. This is done by introducing new representation schemes for fuzzy membership functions and fuzzy rules. An effectiveness measure for fuzzy rules is developed that allows for systematic addition or deletion of rules during the GA optimization process. A clustering method is utilized for generating new rules to be added when additions are required. The performance of the classifier is tested on two real-world databases (Iris and Wine) and a simulated Gaussian database. The results indicate that highly accurate classifiers could be designed with relatively few fuzzy rules. The performance is also compared to other fuzzy classifiers tested on the same databases.     

Applying a genetic algorithm to the optimization of composite patches

This paper describes the application of genetic algorithms (GAs) to the optimization of a composite patch bonded on a metallic structure. The objective is to reduce the stress level in a given area under some constraints such as a maximum surface of the patch and some forbidden zones which cannot be covered by the patch. GAs are presently used for optimizing ply orientations of the stacking sequence as well as for the location and the shape of the patch which is modelled with a spline function. The design variables are ply orientations and coordinates of interpolation points which define a closed plane spline curve. Stress field calculations in the structure are carried out using the Ansys finite element package. The structure considered in this study is an aluminium plate with a circular hole around which the stress level must be reduced by bonding the composite patch. Shapes and ply stacking sequences resulting from the optimization procedure enable a local reinforcement in the neighbourhood of the patch and also a long-distance effect which consists in a deviation of the stress flow adjoining the area over which the stress level must be reduced.