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检索条件"主题词=evolutionary computation"

共 14936 条记录，以下是411-420 订阅

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evolutionary computation with extinction: experiments and analysis

Evolutionary computation with extinction: experiments and an...

引用

Congress on evolutionary computation

作者： G.B. Fogel G.W. Greenwood K. Chellapilla Natural Selection Inc. Jolla CA USA Department of Electrical & Computer Engineering Portland State University Portland OR USA

Under a species-level abstraction of classical evolutionary programming, the standard tournament selection model is not appropriate. When viewed in this manner, it is more appropriate to consider two modes of life histories: background evolution and extinction. The utility of this approach as an optimization procedure is evaluated on a series of test functions relative to the performance of classical evolutionary programming and fast evolutionary programming. The results indicate that on some smooth, convex landscapes and over noisy, highly multimodal landscapes, extinction evolutionary programming can outperform classical and fast evolutionary programming. On other landscapes, however, extinction evolutionary programming performs considerably worse than classical and fast evolutionary programming. Potential reasons for this variability in performance are indicated.

关键词： evolutionary computation Genetic programming Testing History Functional programming Utility programs Earth Robustness

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evolutionary computation for many-objective optimization problems using massive population sizes on the K supercomputer

Evolutionary computation for many-objective optimization pro...

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Congress on evolutionary computation

作者： Tomoaki Tatsukawa Takeshi Watanabe Akira Oyama Tokyo University of Science Shinjuku-ku Tokyo Japan ISAS/JAXA Sagamihara Kanagawa Japan

ISBN: (纸本)9781509006243

The advantages of evolutionary computation with very large populations for many-objective optimization problems are investigated. The effects of a population size of up to 1,000,000 are studied, with the number of generations fixed at 100. To overcome difficulty in computational time, we use a many-objective evolutionary algorithm designed for massive parallelization (CHEETAH) on the K supercomputer. For unimodal test problems DTLZ2 and DTLZ4, the inverted generational distance (IGD) decreases as the population increases while the generational distance (GD) is saturated with a population size of 10,000. This means an evolutionary computation with massive population size mainly contributes to improvement of diversity of obtained non-dominated solutions. Even when the total number of evaluations is fixed, this conclusion is unchanged. For the multimodal test problems DTLZ1 and DTLZ3, GD and IGD are reduced with increasing population size of up to 10,000 but are not significantly improved with population sizes larger than this. This is probably due to the difficulty in obtaining good non-dominated solutions for DTLZ1 and DTLZ3 with current CHEETAH. Because CHEETAH is bases on NSGA-II (only the non-dominated sort portion is modified for more effective many-objective optimization and parallelization), we expect that the current conclusion qualitatively stays the same for other NSGA-II-based algorithms. To take advantage of the larger population size, development of operators such as selection and crossover designed for very large population size may be required.

关键词： Sociology Statistics evolutionary computation Chebyshev approximation Optimization Supercomputers Additives

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evolutionary computation in identification of a tumor

Evolutionary computation in identification of a tumor

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Congress on evolutionary computation

作者： T. Burczynski W. Kus E. Majchrzak P. Orantek M. Dziewonski Department for Strength of Materials and Computational Mechanics Silesian University of Technology Gliwice Poland

The paper deals with an application of evolutionary computation in identification of shape and position of a tumor region in the biological tissue domain. The problem is formulated as an inverse problem that is solved by the minimization of a functional formulated as some distance between measured and computed skin surface temperatures. The functional is considered as the fitness function and the minimization is performed by an evolutionary algorithm with the floating point representation of chromosomes. Geometrical parameters or shape and position of the tumor play the role of genes. The evaluation of the fitness function is preceded by the solution of the direct problem for the bioheat transfer (the Pennes equation) by means or the finite element method. Numerical examples of evolutionary computation for 2-D problems are presented.

关键词： evolutionary computation Neoplasms Shape Biological tissues Inverse problems Biology computing Skin Temperature Minimization methods Biological cells

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evolutionary computation techniques and their applications

Evolutionary computation techniques and their applications

引用

IEEE International Conference on Intelligent Processing Systems, ICIPS

作者： Z. Michalewicz M. Michalewicz Computer Science Department University of North Carolina Charlotte Charlotte NC USA Institute of Computer Science Polish Academy of Sciences Warsaw Poland

evolutionary computation techniques, which are based on a powerful principle of evolution: survival of the fittest, constitute an interesting category of heuristic search. evolutionary computation techniques are stochastic algorithms whose search methods model some natural phenomena: genetic inheritance and Darwinian strive for survival. This paper discusses the main paradigms of evolutionary computation techniques (genetic algorithms, evolution strategies, evolutionary programming, genetic programming) as well as other methods, which are hard to classify. It addresses an important question "why evolutionary computation?" and discusses some applications of these techniques.

关键词： evolutionary computation Books Genetic algorithms Data structures Simulated annealing Tellurium Genetic mutations Process control Scattering

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evolutionary computation technologies for space systems

Evolutionary computation technologies for space systems

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IEEE Conference on Aerospace

作者： R.J. Terrile C. Adami H. Aghazarian S.N. Chau V.T. Dang M.I. Ferguson W. Fink T.L. Huntsberger G. Klimeck M.A. Kordon Seungwon Lee P. von Allmen J. Xu Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Jet Propulsion Laboratory California Institute of Technology Pasadena CA

The Evolvable computation Group, at NASA's Jet Propulsion Laboratory, is tasked with demonstrating the utility of computational engineering and computer optimized design for complex space systems. The group is comprised of researchers over a broad range of disciplines including biology, genetics, robotics, physics, computer science and system design, and employs biologically inspired evolutionary computational techniques to design and optimize complex systems. Over the past two years we have developed tools using genetic algorithms, simulated annealing and other optimizers to improve on human design of space systems. We have further demonstrated that the same tools used for computer-aided design and design evaluation can be used for automated innovation and design. These powerful techniques also serve to reduce redesign costs and schedules

关键词： Space technology evolutionary computation Biology computing Design optimization Propulsion Laboratories Design engineering computational systems biology Evolution (biology) Genetics

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evolutionary computation techniques for behaviour fusion in autonomous mobile robots

Evolutionary computation techniques for behaviour fusion in ...

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Congress on evolutionary computation

作者： H. Martinez A. Gomez-Skarmeta F. Jimenez M. Zamora Department Informática Inteligencia Artificial y Electrónica Universidad de Murcia Murcia Spain

In this paper, we present evolutionary techniques to solve the problem of the conflicts between different behaviours in the context of an autonomous mobile robot. We also describe the working environment, based on a custom programming language (named BG after its inventors, Barber/spl acute/a and Go/spl acute/mez, 1996) and an agent architecture, where we test a series of behaviours that were developed using fuzzy logic. Finally, some results related to a simple navigational task in an unknown environment are presented.

关键词： evolutionary computation Mobile robots Navigation Robot kinematics Computer languages Robot sensing systems Robot control Logic testing Fuzzy logic Uncertainty

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evolutionary computation with Habitats for Finding Kubelka-Munk Coefficients in Metallic and Pearlescent Paints

Evolutionary Computation with Habitats for Finding Kubelka-M...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Eric W. Cooper Yoshiaki Sakakura Yasuhiro Ohta Kozo Furukawa Hiroyuki Shinoda Kozaburo Hachimura College of Information Science and Engineering Ritsumeikan University Shiga Japan Center of Isamu Paint Company Limited Shiga Japan College of Information Science and Engineering Ritsumeikan University Japan

This paper describes an application of evolutionary computing to determining appropriate scattering and absorption coefficients in Kubelka-Munk equations for a set of colorants used in paints. The method describes treatment of the solution spaces as a group of environmental habitats in which constant pairs can move from habitat to habitat and survive where they are successful in determining the behavior of the colorants used. This method was applied and tested on a set of 111 paint samples mixed with components including metallic and pearlescent colorants. The method obtained appropriate coefficients for the paint set without any prior determination of the scattering and absorption of the colorants on standard white or black backings.

关键词： evolutionary computation Paints Absorption Light scattering Equations Educational institutions Information science Reflectivity Light sources Fluorescence

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evolutionary computation for feature manipulation: Key challenges and future directions

Evolutionary computation for feature manipulation: Key chall...

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Congress on evolutionary computation

作者： Bing Xue Mengjie Zhang School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9781509006243

In machine learning and data mining, feature manipulation is a data pre-processing step to increase the quality of a feature space, which can significantly improve the performance of a learning algorithm in terms of the accuracy, the learning speed, and the complexity and the interpretability of the learnt models. However, feature manipulation is a difficult task and facing more challenges along with the trend that more and more data is collected in many domains. evolutionary computation (EC) techniques have recently attracted much attention for dealing with complex feature manipulation problems. Current work has demonstrated some strengths of EC for feature manipulation, but also shown some limitations and issues that need to be addressed. More importantly, there are some highly interesting research topics in the EC for feature manipulation area, which could potentially result in promising approaches to data analysis in a variety of real-world applications. This position paper describes and discusses the main issues and key challenges of feature manipulation, and also provides a number of directions for further consideration in future research.

关键词： Feature extraction Data mining Machine learning algorithms Search problems evolutionary computation Computer science Optimization

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evolutionary computation for topological optimization of 3-connected computer networks

Evolutionary computation for topological optimization of 3-c...

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IEEE International Conference on Systems, Man and Cybernetics

作者： B. Ombuki M. Nakamura Z. Nakao K. Onaga Faculty of Engineering University of Ryukyus Okinawa Japan

In the 3-connected computer network design problem, a set of links is to be assigned to some computer sites (nodes) such that every source-destination pair of nodes can successfully communicate with each other via at least one of three diverse paths. The objective is to minimize the total link connection costs while maintaining the 3-connectivity constraint. We develop two heuristics, a greedy heuristic and a genetic algorithm (GA) based heuristic to approximately solve the design problem. An experimental evaluation shows that the GA consistently outperforms the greedy algorithm for the problem instances considered.

关键词： evolutionary computation Computer networks Algorithm design and analysis Communication networks Genetic algorithms Computer network reliability Costs Greedy algorithms Spine Telecommunication network reliability

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evolutionary computation for Optimal Component Deployment with Multitenancy Isolation in Cloud-hosted Applications

Evolutionary Computation for Optimal Component Deployment wi...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Laud Charles Ochei Andrei Petrovski Julian M. Bass School of Computing Science and Digital Media Robert Gordon University Aberdeen United Kingdom School of Computing Science and Engineering University of Salford Manchester United Kingdom

ISBN: (纸本)9781538651513

A multitenant cloud-application that is designed to use several components needs to implement the required degree of isolation between the components when the workload changes. The highest degree of isolation results in high resource consumption and running cost per component. A low degree of isolation allows sharing of resources, but leads to degradation in performance and to increased security vulnerability. This paper presents a simulation-based approach operating on computational metaheuristics that search for optimal ways of deploying components of a cloud-hosted application to guarantee multitenancy isolation When the workload changes, an open multiclass Queuing Network model is used to determine the average number of component access requests, followed by a metaheuristic search for the optimal deployment solutions of the components in question. The simulation-based evaluation of optimization performance showed that the solutions obtained were very close to the target solution. Various recommendations and best practice guidelines for deploying components in a way that guarantees the required degree of isolation are also provided.

关键词： Mathematical model computational modeling Cloud computing Simulated annealing evolutionary computation Software

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