检索结果-内蒙古大学图书馆

Evolutionarily stable sets in quantum penny flip games

QUANTUM INFORMATION PROCESSING 2013年第6期12卷 2143-2165页

作者： Yu, Tina Ben-Av, Radel Mem Univ Newfoundland Dept Comp Sci St John NF A1B 3X5 Canada Jerusalem Coll Engn Dept Software Engn Jerusalem Israel

In game theory, an Evolutionarily Stable Set (ES set) is a set of Nash Equilibrium (NE) strategies that give the same payoffs. Similar to an Evolutionarily Stable Strategy (ES strategy), an ES set is also a strict NE. This work investigates the evolutionary stability of classical and quantum strategies in the quantum penny flip games. In particular, we developed an evolutionary game theory model to conduct a series of simulations where a population of mixed classical strategies from the ES set of the game were invaded by quantum strategies. We found that when only one of the two players' mixed classical strategies were invaded, the results were different. In one case, due to the interference phenomenon of superposition, quantum strategies provided more payoff, hence successfully replaced the mixed classical strategies in the ES set. In the other case, the mixed classical strategies were able to sustain the invasion of quantum strategies and remained in the ES set. Moreover, when both players' mixed classical strategies were invaded by quantum strategies, a new quantum ES set was emerged. The strategies in the quantum ES set give both players payoff 0, which is the same as the payoff of the strategies in the mixed classical ES set of this game.

关键词： Quantum games evolutionary game theory Evolutionarily stable strategies Evolutionarily stable sets evolutionary algorithms Quantum penny flip games

来源：评论

学校读者我要写书评

暂无评论

A genetic iterated greedy algorithm for the blocking flowshop to minimize total earliness and tardiness

引用

JOURNAL OF INTELLIGENT MANUFACTURING 2024年第5期35卷 2161-2174页

作者： Prata, Bruno de Athayde Fuchigami, Helio Yochihiro Univ Fed Ceara Dept Ind Engn Fortaleza CE Brazil Univ Fed Sao Carlos Prod Engn Dept Sao Carlos Brazil

An important and realistic class of scheduling problems is considered in this paper: the total earliness and tardiness minimization in the blocking flowshop, where there is no intermediate buffer between machines. Blocking occurs when a completed item or product remains on the machine until the next machine is available. We proposed a new hybrid evolutionary algorithm: the Genetic Iterated Greedy Algorithm (GIGA). In our innovative solution approach, a genetic algorithm presents a hybrid crossover based on the Iterated Greedy metaheuristic. The hybrid crossover considers the Hamming distance as an indicator of the diversity of the current population. In the first generations, the crossover will adopt larger values for the destruction parameter, and this value is gradually reduced throughout the search process. Our proposal is compared to four competitive metaheuristics reported for earliness and tardiness flowshop. Two performance indicators are considered: the Average Relative Percentage Deviation (ARPD) and the Success Rate (SR). Based on the statistical analysis of the computational experimentation, our GIGA outperformed all the implemented algorithms of the literature with statistical significance. Concerning the performance indicators, GIGA achieved ARPD = 0.02% and SR = 83.5%, pointing to the superiority of the proposed solution approach.

关键词： Production sequencing Just-in-time Metaheuristics evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

A study of advanced high-loaded transonic turbine airfoils

引用

JOURNAL OF TURBOMACHINERY-TRANSACTIONS OF THE ASME 2006年第4期128卷 650-657页

作者： Sonoda, Toyotaka Arima, Toshiyuki Olhofer, Markus Sendhoff, Bernhard Kost, Friedrich Giess, P-A. Honda Res & Dev Co Ltd Aircraft Engn R&D Ctr Saitama 3510193 Japan Honda Res & Dev Co Ltd Wako Res Ctr Saitama 3510193 Japan Honda Res Inst Europe GmbH D-63073 Offenbach Germany Inst Propuls Technol German Aerospace Ctr DLR D-37073 Gottingen Germany

The development of high-performance turbine airfoils has been investigated under the condition of a supersonic exit Mach number In order to obtain a new aerodynamic design concept for a high-loaded turbine rotor blade, we employed an evolutionary algorithm for numerical optimization. The target of the optimization method, which is called evolutionary strategy (ES), was the minimization of the total pressure loss and the deviation angle. The optimization process includes the representation of the airfoil geometry, the generation of the grid for a blade-to-blade computational fluid dynamics analysis, and a two-dimensional Navier-Stokes solver with a low-Re k-epsilon turbulence model in order to evaluate the performance. Some interesting aspects, for example, a double shock system, an early transition, and a redistribution of aerodynamic loading on blade surface, observed in the optimized airfoil, are discussed. The increased performance of the optimized blade has been confirmed by detailed experimental investigation, using conventional probes, hotfilms, and L2F system.

关键词： Optimization transonic flow computational fluid dynamics rotors Turbines turbulence blades supersonic flow evolutionary algorithms Turbulence Cascades (Fluid dynamics) Blades Mach number Boundary layers Flow (Dynamics) Shock (Mechanics) Pressure Suction minimisation Rotors evolutionary computation Navier-Stokes equations turbines Design Probes Computational fluid dynamics Airfoils aerodynamics

来源：评论

学校读者我要写书评

暂无评论

Abandoning Objectives: Evolution Through the Search for Novelty Alone

引用

evolutionary COMPUTATION 2011年第2期19卷 189-223页

作者： Lehman, Joel Stanley, Kenneth O. Univ Cent Florida Sch Elect Engn & Comp Sci Orlando FL 32816 USA

In evolutionary computation, the fitness function normally measures progress toward an objective in the search space, effectively acting as an objective function. Through deception, such objective functions may actually prevent the objective from being reached. While methods exist to mitigate deception, they leave the underlying pathology untreated: Objective functions themselves may actively misdirect search toward dead ends. This paper proposes an approach to circumventing deception that also yields a new perspective on open-ended evolution. Instead of either explicitly seeking an objective or modeling natural evolution to capture open-endedness, the idea is to simply search for behavioral novelty. Even in an objective-based problem, such novelty search ignores the objective. Because many points in the search space collapse to a single behavior, the search for novelty is often feasible. Furthermore, because there are only so many simple behaviors, the search for novelty leads to increasing complexity. By decoupling open-ended search from artificial life worlds, the search for novelty is applicable to real world problems. Counterintuitively, in the maze navigation and biped walking tasks in this paper, novelty search significantly outperforms objective-based search, suggesting the strange conclusion that some problems are best solved by methods that ignore the objective. The main lesson is the inherent limitation of the objective-based paradigm and the unexploited opportunity to guide search through other means.

关键词： evolutionary algorithms deception novelty search open-ended evolution neuroevolution

来源：评论

学校读者我要写书评

暂无评论

A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands

引用

COMPUTERS & OPERATIONS RESEARCH 2010年第11期37卷 1886-1898页

作者： Mendoza, Jorge E. Castanier, Bruno Gueret, Christelle Medaglia, Andres L. Velasco, Nubia Ecole Mines Nantes IRCCyN Equipe Syst Logist & Prod CNRSUMR 6597 F-44307 Nantes 3 France Univ Los Andes COPA Dept Ind Engn Bogota Colombia

The multi-compartment vehicle routing problem (MC-VRP) consists of designing transportation routes to satisfy the demands of a set of customers for several products that, because of incompatibility constraints, must be loaded in independent vehicle compartments. Despite its wide practical applicability the MC-VRP has not received much attention in the literature, and the few existing methods assume perfect knowledge of the customer demands, regardless of their stochastic nature. This paper extends the MC-VRP by introducing uncertainty on what it is known as the MC-VRP with stochastic demands (MC-VRPSD). The MC-VRPSD is modeled as a stochastic program with recourse and solved by means of a memetic algorithm. The proposed memetic algorithm couples genetic operators and local search procedures proven to be effective on deterministic routing problems with a novel individual evaluation and reparation strategy that accounts for the stochastic nature of the problem. The algorithm was tested on instances of up to 484 customers, and its results were compared to those obtained by a savings-based heuristic and a memetic algorithm (MA/SCS) for the MC-VRP that uses a spare capacity strategy to handle demand fluctuations. In addition to effectively solve the MC-VRPSD, the proposed MA/SCS also improved 14 best known solutions in a 40-problem testbed for the MC-VRP. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Multi-compartment vehicle routing problem Stochastic demands evolutionary algorithms Memetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Policy planning under uncertainty: Efficient starting populations for simulation-optimization methods applied to municipal solid waste management

引用

JOURNAL OF ENVIRONMENTAL MANAGEMENT 2005年第1期77卷 22-34页

作者： Huang, GH Linton, JD Yeomans, JS Yoogalingam, R York Univ Schulich Sch Business Management Sci Area Toronto ON M3J 1P3 Canada Univ Regina Fac Engn Regina SK S4S 0A2 Canada Rensselaer Polytech Inst Lally Sch Management Troy NY 12180 USA Brock Univ Dept Management Fac Business St Catharines ON L2S 3A1 Canada

evolutionary simulation-optimization (ESO) techniques can be adapted to model a wide variety of problem types in which system components are stochastic. Grey programming (GP) methods have been previously applied to numerous environmental planning problems containing uncertain information. In this paper, ESO is combined with GP for policy planning to create a hybrid solution approach named GESO. It can be shown that multiple policy alternatives meeting required system criteria, or modelling-to-generate-alternatives (MGA), can be quickly and efficiently created by applying GESO to this case data. The efficacy of GESO is illustrated using a municipal solid waste management case taken from the regional municipality of Hamilton-Wentworth in the Province of Ontario, Canada. The MGA capability of GESO is especially meaningful for large-scale real-world planning problems and the practicality of this procedure can easily be extended from MSW systems to many other planning applications containing significant sources of uncertainty. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： public sector decision making uncertainty simulation evolutionary algorithms waste management planning modelling to generate alternatives

来源：评论

学校读者我要写书评

暂无评论

Optimisation techniques for electrical power systems - Part 2 - Heuristic optimisation methods

引用

POWER ENGINEERING JOURNAL 2001年第3期15卷 151-160页

作者： Song, YH Irving, MR Brunel Univ Dept Elect & Comp Engn Uxbridge UB8 3PH Middx England

An introduction to mathematical programming based methods was given in the first tutorial of this three-part series (October 2000 PEJ, p.245). This second part covers major modern heuristic optimisation techniques and their integration and comparison with other methods. The third and last tutorial will consider full-scale power system application examples.

关键词： electrical power systems fuzzy set theory neural networks Power engineering computing mathematical programming power systems Optimisation search problems tabu search Combinatorial mathematics simulated annealing genetic algorithms heuristic optimisation methods power system analysis computing Power systems fuzzy programming neural nets evolutionary algorithms ant colony search Optimisation techniques Neural computing techniques

来源：评论

学校读者我要写书评

暂无评论

A migration strategy of mobile agents for the transport network applications

引用

MATHEMATICS AND COMPUTERS IN SIMULATION 2008年第5-6期76卷 345-362页

作者： Zgaya, Hayfa Hammadi, Slim Ghedira, Khaled Ecole Cent Lille LAGIS UMR 8146 Lille France Tunis Univ ISG SOIE LAGIS Tunis Tunisia Tunis Univ ISG ISG Tuni SOIE Tunis Tunisia

This paper presents a migration strategy for a set of mobile agents (MAs) in order to satisfy customers' requests in a transport network, through a multimodal information system. In this context, we propose an optimization solution which operates on two levels. The first one aims to constitute a set of MAs building their routes, called Workplans. At this stage, Workplans must incorporate all nodes, representing information providers in the multimodal network, in order to explore it completely. Thanks to an evolutionary approach, the second level must optimize nodes' selection in order to increase the number of satisfied users. The assignment of network nodes to the required services must be followed by a Workplan update procedure in order to deduce final routes paths. Finally, simulation results are mentioned to invoke the different steps of our adopted approach. (c) 2007 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： mobile agents evolutionary algorithms multimodal information system multimodal transport network

来源：评论

学校读者我要写书评

暂无评论

An adaptive-topology ensemble algorithm for engineering optimization problems

引用

OPTIMIZATION AND ENGINEERING 2015年第2期16卷 303-334页

作者： Tenne, Yoel Ariel Univ Dept Mech & Mech Engn Ariel Israel

Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a scenario which formulates the problem of optimizing a computationally expensive black-box functions. In such problems, there will often exist candidate designs which cause the simulation to fail, and this can degrade the optimization effectiveness. To address this issue, this paper proposes a new optimization algorithm which incorporates classifiers into the optimization search. The classifiers predict which candidate design are expected to cause the simulation to fail, and their prediction is used to bias the search towards valid designs, namely, for which the simulation is expected to succeed. However, the effectiveness of this approach strongly depends on the type of metamodels and classifiers being used, but due to the high cost of evaluating the simulation-based objective function it may be impractical to identify by numerical experiments the most suitable types of each. Leveraging on these issues, the proposed algorithm offers two main contributions: (a) it uses ensembles of both metamodels and classifiers to benefit from a diversity of predictions of different metamodels and classifiers, and (b) to improve the search effectiveness, it continuously adapts the ensembles' topology during the search. The performance of the proposed algorithm was evaluated using an engineering problem of airfoil shape optimization. Performance analysis of the proposed algorithm using an engineering problem of airfoil shape optimization shows that: (a) incorporating classifiers into the search was an effective approach to handle simulation failures (b) using ensembles of metamodels and classifiers, and updating their topology during the search, improved the search effectiveness in comparison to using a single metamodel and classifier, and (c) it is beneficial to update the topology of the metamodel ensemble in all problem types, and it is beneficial to update the classifier

关键词： Expensive optimization problems evolutionary algorithms Meta-modelling Classification

来源：评论

学校读者我要写书评

暂无评论

Genetic Programming: A new paradigm in rainfall runoff modeling

引用

JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 2002年第3期38卷 705-718页

作者： Liong, SY Gautam, RR Khu, ST Babovic, V Keijzer, M Muttil, N Natl Univ Singapore Dept Civil Engn Singapore 119260 Singapore DHI Water & Environm DK-2970 Horsholm Denmark

Genetic Programming (GP) is a domain-independent evolutionary programming technique that evolves computer programs to solve, or approximately solve, problems. To verify GP's capability, a simple example with known relation in the area of symbolic regression, is considered first. GP is then utilized as a flow forecasting tool. A catchment in Singapore with a drainage area of about 6 km(2) is considered in this study. Six storms of different intensities and durations are used to train GP and then verify the trained GP Analysis of the GP induced rainfall and runoff relationship shows that the cause and effect relationship between rainfall and runoff is consistent with the hydrologic process. The result shows that the runoff prediction accuracy of symbolic regression based models, measured in terms of root mean square error and correlation coefficient, is reasonably high. Thus, GP induced rainfall runoff relationships can be a viable alternative to traditional rainfall runoff models.

关键词： genetic programming evolutionary algorithms rainfall-runoff relationships runoff forecasting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：