使用BSP和遗传算法的图像稀疏化技术

图像稀疏化技术是利用图像中稀少的且与具体应用相关的数据来表示原始图像的技术.使用BSP和遗传算法的方法在图像中生成能够近似图像的自适应的网格,即用较少的包含重要信息的像素来表示图像,实现图像的稀疏化,达到压缩之目的.该自适应网格能够以很高的质量重构出原始图像,在图像处理和计算机视觉领域有很好的应用前景.     

Experimental study of seeding in genetic algorithms with non-binary genetic representation

Seeding is a technique used to leverage population diversity in genetic algorithms. This paper presents a quick survey of different seeding approaches, and evaluates one of the promising ones called the Seeding Genetic Algorithm. The Seeding GA does not include mutation, and it has been shown to work well on some GA-hard problems with binary representation, such as the Hierarchical If-and-Only-If or Deceptive Trap. This paper investigates the effectiveness of the Seeding GA on two problems with more complex non-binary representations: capacitated lot-sizing and single-machine scheduling. The results show, with statistical significance, that the new GA is consistently outperformed by the conventional GA, and that not including mutation is the main reason why. A detailed analysis of the results is presented and suggestions are made to enhance and improve the method.     

Improving swarming using genetic algorithms

The verification of temporal properties against a given system may require the exploration of its full state space. In explicit model checking, this exploration uses a depth-first search and can be achieved with multiple randomized threads to increase performance. Nonetheless, the topology of the state space and the exploration order can cap the speedup up to a certain number of threads. This paper proposes a new technique that aims to tackle this limitation by generating artificial initial states, using genetic algorithms. Threads are then launched from these states and thus explore different parts of the state space. Our prototype implementation is 10% faster than state-of-the-art algorithms on a general benchmark and 40% on a specialized benchmark. Even if we expected a decrease in an order of magnitude, these results are still encouraging since they suggest a new way to handle existing limitations. Empirically, our technique seems well suited for “linear” topology, i.e., the one we can obtain when combining model checking algorithms with partial-order reduction techniques.

     

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.     

Shape analysis using genetic algorithms

This paper introduces a novel methodology for shape discrimination by combining pattern recognition techniques such as morphological processing with concepts from artificial intelligence and machine learning such as genetic algorithms (GAs). High-performance shape discrimination operators, defined as variable structuring elements and sequenced as program forms, are derived using GAs. The population of operators, iteratively evaluated according to an performance index corresponding to shape discrimination ability, evolves into an optimal set of operators using the evolutionary principles of genetic search. Experimental results are included to illustrate the feasibility of our novel methodology for developing robust shape analysis methods.     

Dimensionality reduction using genetic algorithms

Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern affect the success of subsequent classification. Feature extraction is the process of deriving new features from original features to reduce the cost of feature measurement, increase classifier efficiency, and allow higher accuracy. Many feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and classification efficiency, it does not necessarily reduce the number of features to be measured since each new feature may be a linear combination of all of the features in the original pattern vector. Here, we present a new approach to feature extraction in which feature selection and extraction and classifier training are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a feature weight vector used to scale the individual features in the original pattern vectors. A masking vector is also employed for simultaneous selection of a feature subset. We employ this technique in combination with the k nearest neighbor classification rule, and compare the results with classical feature selection and extraction techniques, including sequential floating forward feature selection, and linear discriminant analysis. We also present results for the identification of favorable water-binding sites on protein surfaces     

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.     

Acceleration of cellular automata algorithms using genetic algorithms

The following problem is solved: Given a Cellular Automaton with continuous state space which simulates a physical system or process, use a Genetic Algorithm in order to find a Cellular Automaton with discrete state space, having the smallest possible lattice size and the smallest possible number of discrete states, the results of which are as close as possible to the results of the Cellular Automaton with continuous state space. The Cellular Automaton with discrete state space evolves much faster than the Cellular Automaton with continuous state space. The state spaces of two Cellular Automata have been discretized using a Genetic Algorithm. The first Cellular Automaton simulates the two-dimensional photoresist etching process in integrated circuit fabrication and the second is used to predict forest fire spreading. A general method for the discretization of the state space of Cellular Automata using a Genetic Algorithm is also presented. The aim of this work is to provide a method for accelerating the execution of algorithms based on Cellular Automata (Cellular Automata algorithms) and to build a bridge between Cellular Automata as models for physical systems and processes and Cellular Automata as a VLSI architecture.     

Robust manufacturing system design using multi objective genetic algorithms,Petri nets and Bayesian uncertainty representation

Decisions involving robust manufacturing system configuration design are often costly and involve long term allocation of resources. These decisions typically remain fixed for future planning horizons and failure to design a robust manufacturing system configuration can lead to high production and inventory costs, and lost sales costs. The designers need to find optimal design configurations by evaluating multiple decision variables (such as makespan and WIP) and considering different forms of manufacturing uncertainties (such as uncertainties in processing times and product demand). This paper presents a novel approach using multi objective genetic algorithms (GA), Petri nets and Bayesian model averaging (BMA) for robust design of manufacturing systems. The proposed approach is demonstrated on a manufacturing system configuration design problem to find optimal number of machines in different manufacturing cells for a manufacturing system producing multiple products. The objective function aims at minimizing makespan, mean WIP and number of machines, while considering uncertainties in processing times, equipment failure and repairs, and product demand. The integrated multi objective GA and Petri net based modeling framework coupled with Bayesian methods of uncertainty representation provides a single tool to design, analyze and simulate candidate models while considering distribution model and parameter uncertainties.     

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.     

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.     

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 genetic algorithms using discretization scheduling

In many applications of genetic algorithms, there is a tradeoff between speed and accuracy in fitness evaluations when evaluations use numerical methods with varying discretization. In these types of applications, the cost and accuracy vary from discretization errors when implicit or explicit quadrature is used to estimate the function evaluations. This paper examines discretization scheduling, or how to vary the discretization within the genetic algorithm in order to use the least amount of computation time for a solution of a desired quality. The effectiveness of discretization scheduling can be determined by comparing its computation time to the computation time of a GA using a constant discretization. There are three ingredients for the discretization scheduling: population sizing, estimated time for each function evaluation and predicted convergence time analysis. Idealized one- and two-dimensional experiments and an inverse groundwater application illustrate the computational savings to be achieved from using discretization scheduling.     

遗传算法在货运车辆优化调度中的应用

本文在阐述了遗传算法基本理论和车辆优化调度基本理论的基础上,进一步论述了遗传算法在一般车辆优化调度中的应用。参考近年来遗传算法应用于车辆优化调度的一些文献,对应用于不同情况下货运车辆优化调度的遗传算法进行了总结和分析。最后对本文进行总结,并对未来的遗传算法在货运车辆优化调度中的应用提出了发展趋势。     

Testing real-time systems using genetic algorithms

The development of real-time systems is an essential industrial activity whose importance is increasing. The most important analytical method to assure the quality of real-time systems is dynamic testing. Testing is the only method which examines the actual run-time behaviour of real-time software, based on an execution in the real application environment. Dynamic aspects like the duration of computations, the memory actually needed, or the synchronization of parallel processes are of major importance for the correct function of real-time systems and have to be tested. A comprehensive investigation of existing software test methods shows that they mostly concentrate on testing for functional correctness. They are not suited for an examination of temporal correctness which is essential to real-time systems. Very small systems show a wide range of different execution times. Therefore, existing test procedures must be supplemented by new methods, which concentrate on determining whether the system violates its specified timing constraints. In general, this means that outputs are produced too early or their computation takes too long. The task of the tester is to find the inputs with the longest or shortest execution times to check whether they produce a temporal error. If the search for such inputs is interpreted as a problem of optimization, genetic algorithms can be used to find the inputs with the longest or shortest execution times automatically. The fitness function is the execution time measured in processor cycles. Experiments using genetic algorithms on a number of programs with up to 1511 LOC and 843 integer input parameters have successfully identified new longer and shorter paths than had been found using random testing or systematic testing. Genetic algorithms are able therefore to check large programs and they show considerable promise in establishing the validity of the temporal behaviour of real-time software.     

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.     

Assembly line balancing using genetic algorithms

Assembly Line Balancing (ALB) is one of the important problems of production/operations management area. As small improvements in the performance of the system can lead to significant monetary consequences, it is of utmost importance to develop practical solution procedures that yield high-quality design decisions with minimal computational requirements. Due to the NP-hard nature of the ALB problem, heuristics are generally used to solve real life problems. In this paper, we propose an efficient heuristic to solve the deterministic and single-model ALB problem. The proposed heuristic is a Genetic Algorithm (GA) with a special chromosome structure that is partitioned dynamically through the evolution process. Elitism is also implemented in the model by using some concepts of Simulated Annealing (SA). In this context, the proposed approach can be viewed as a unified framework which combines several new concepts of AI in the algorithmic design. Our computational experiments with the proposed algorithm indicate that it outperforms the existing heuristics on several test problems.     

RGFGA: an efficient representation and crossover for grouping genetic algorithms

There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree.We introduce a new representation for grouping genetic algorithms, the restricted growth function genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman optimisation method and a well-established statistical clustering algorithm, with encouraging results.