检索结果-内蒙古大学图书馆

Modeling and convergence analysis of distributed coevolutionary algorithms

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2004年第2期34卷 806-822页

作者： Subbu, R Sanderson, AC Gen Elect Global Res Niskayuna NY 12309 USA Rensselaer Polytech Inst Troy NY 12180 USA

A theoretical foundation is presented for modeling and convergence analysis of a class of distributed coevolutionary algorithms applied to optimization problems in which the variables are partitioned among p nodes. An evolutionary algorithm at each of the p nodes performs a local evolutionary search based on its own set of primary variables, and the secondary variable set at each node is clamped during this phase. An infrequent intercommunication between the nodes updates the secondary variables at each node. The local search and intercommunication phases alternate, resulting in a cooperative search by the p nodes. First, we specify a theoretical basis for a class of centralized evolutionary algorithms in terms of construction and evolution of sampling distributions over the feasible space. Next, this foundation is extended to develop a model for a class of distributed coevolutionary algorithms. Convergence and convergence rate analyzes are pursued for basic classes of objective functions. Our theoretical investigation reveals that for certain unimodal and multimodal objectives, we can expect these algorithms to converge at a geometrical rate. The distributed coevolutionary algorithms are of most interest from the perspective of their performance advantage compared to centralized algorithms, when they execute in a network environment with significant local access and internode communication delays. The relative performance of these algorithms is therefore evaluated in a distributed environment with realistic parameters of network behavior.

关键词： coevolutionary algorithms modeling and convergence analysis network behavior population distributions theoretical foundation

来源：评论

学校读者我要写书评

暂无评论

Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks

引用

APPLIED SOFT COMPUTING 2020年 87卷 105990-000页

作者： Hultmann Ayala, Helon Vicente Habineza, Didace Rakotondrabe, Micky Coelho, Leandro dos Santos Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Mech Engn Rua Marques de Sao Vicente 225 BR-22453900 Rio De Janeiro RJ Brazil Punch Powertrain Nv Ondernemerslaan 5429 B-3800 St Truiden Belgium Toulouse INP LGP Natl Sch Engn Tarbes ENIT 47 Ave Azreix F-65000 Tarbes France Pontifical Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program Rua Imaculada Conceicao 1155 BR-80215910 Curitiba Parana Brazil Fed Univ Parana UFPR Dept Elect Engn Rua Cel Francisco Heraclito dos Santos 100 BR-81531980 Curitiba Parana Brazil

The present work deals with the application of coevolutionary algorithms and artificial neural networks to perform input selection and related parameter estimation for nonlinear black-box models in system identification. In order to decouple the resolution of the input selection and parameter estimation, we propose a problem decomposition formulation and solve it by a coevolutionary algorithm strategy. The novel methodology is successfully applied to identify a magnetorheological damper, a continuous polymerization reactor and a piezoelectric robotic micromanipulator. The results show that the method provides valid models in terms of accuracy and statistical properties. The main advantage of the method is the joint input and parameter estimation, towards automating a tedious and error prone procedure with global optimization algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： System identification Nonlinear systems Radial basis functions neural networks coevolutionary algorithms Piezoelectric manipulator

来源：评论

学校读者我要写书评

暂无评论

coevolutionary Memetic algorithms for Solving Traveling Salesman Problem (TSP)

Coevolutionary Memetic Algorithms for Solving Traveling Sale...

引用

作者： Serife Ulucinar Eastern Mediterranean University

学位级别：硕士

In this thesis, coevolutionary Memetic algorithms are used for solving the well- known Traveling Salesman Problem (TSP). Traveling Salesman Problem is NP- Complete which means no algorithm can solve this problem in a computing time that increases polynomially with respect to the problem size. The proposed solution approach to TSP is the combination of coevolutionary algorithms and Memetic algorithms. The objective solution to TSP is to find the minimum tour length that the Traveling Salesman can make under some restrictions. coevolutionary algorithms belong to the class of Evolutionary algorithms. The main difference of coevolutionary algorithms from Evolutionary algorithms is the way of interaction of individuals in the population. The individuals of the population should interact with each other to form a complete solution to the problem. Moreover, Memetic algorithms are hybridized algorithms which combine the Evolutionary algorithms with a local search method. Local search algorithms are the algorithms that refine the solutions by searching within the neighbourhood of promising solutions to find the better ones. In experimental results, the proposed algorithm is tested with several test datas by different parameter values of the algorithm.

关键词： coevolutionary algorithms Memetic algorithms Traveling Salesman Problem

来源：评论

学校读者我要写书评

暂无评论

Multiple Populations for Multiple Objectives: A coevolutionary Technique for Solving Multiobjective Optimization Problems

引用

IEEE TRANSACTIONS ON CYBERNETICS 2013年第2期43卷 445-463页

作者： Zhan, Zhi-Hui Li, Jingjing Cao, Jiannong Zhang, Jun Chung, Henry Shu-Hung Shi, Yu-Hui Sun Yat Sen Univ Guangzhou 510275 Guangdong Peoples R China Minist Educ Key Lab Digital Life Beijing Peoples R China Educ Dept Guangdong Prov Key Lab Software Technol Guangzhou Guangdong Peoples R China Hong Kong Polytech Univ Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China

Traditional multiobjective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multiobjective optimization problems (MOPs). However, this consideration may cause difficulty to assign fitness to individuals because different objectives often conflict with each other. In order to avoid this difficulty, this paper proposes a novel coevolutionary technique named multiple populations for multiple objectives (MPMO) when developing MOEAs. The novelty of MPMO is that it provides a simple and straightforward way to solve MOPs by letting each population correspond with only one objective. This way, the fitness assignment problem can be addressed because the individuals' fitness in each population can be assigned by the corresponding objective. MPMO is a general technique that each population can use existing optimization algorithms. In this paper, particle swarm optimization (PSO) is adopted for each population, and coevolutionary multiswarm PSO (CMPSO) is developed based on the MPMO technique. Furthermore, CMPSO is novel and effective by using an external shared archive for different populations to exchange search information and by using two novel designs to enhance the performance. One design is to modify the velocity update equation to use the search information found by different populations to approximate the whole Pareto front (PF) fast. The other design is to use an elitist learning strategy for the archive update to bring in diversity to avoid local PFs. CMPSO is comprehensively tested on different sets of benchmark problems with different characteristics and is compared with some state-of-the-art algorithms. The results show that CMPSO has superior performance in solving these different sets of MOPs.

关键词： coevolutionary algorithms multiobjective optimization problems (MOPs) multiple populations for multiple objectives (MPMO) particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

A coevolutionary algorithm assisted by two archives for constrained multi-objective optimization problems

引用

SWARM AND EVOLUTIONARY COMPUTATION 2023年 82卷

作者： Zeng, Yong Cheng, Yuansheng Liu, Jun Huazhong Univ Sci & Technol Sch Naval Architecture & Ocean Engn Wuhan 430074 Peoples R China Deep Sea Explorat CISSE Innovat Ctr Adv Ship Shanghai 200240 Peoples R China

coevolutionary algorithms have demonstrated high performance on many constrained multi-objective prob-lems. However, on some problems with fraudulent constraints or small feasible regions, they may fail to converge to the Pareto front (PF) or even fail to find a feasible solution. To search for feasible solutions robustly for various problems and obtain solutions approaching the PF as possible, this paper proposes a coevolutionary algorithm assisted by two archives. To be specific, a population for minimizing constraint violation and a population for optimizing objectives without constraints coevolve to find a feasible solution first. Then, the feasible solutions are improved by the population with constrained dominance principle and an archive consisting of inversely-updated infeasible solutions. In addition to searching in the feasible regions found so far, the unconstrained population continues searching the objectives without constraints, expecting that new feasible regions can be spotted in the promising regions. To mitigate the issues that small feasible regions may be missed by the unconstrained population, a diversity archive is updated in a larger objective space than the unconstrained population to enhance exploration. In the experiments, the proposed method is compared with 11 state-of-the-art algorithms on 67 problems to demonstrate its effectiveness. The results show that the proposed method is very robust in finding feasible solutions and obtains better or competitive performance on most problems.

关键词： Constrained multi-objective optimization coevolutionary algorithms Archive assisting

来源：评论

学校读者我要写书评

暂无评论

Network-based distributed planning using coevolutionary agents: Architecture and evaluation

引用

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2004年第2期34卷 257-269页

作者： Subbu, R Sanderson, AC Gen Elect Global Res Niskayuna NY 12309 USA Rensselaer Polytech Inst Troy NY 12180 USA

A novel evolutionary planning framework (coevolutionary virtual design environment) particularly suited to distributed network-enabled design and manufacturing organizations is presented. The approach utilizes distributed evolutionary agents and mobile agents as principal object-oriented software entities that support a network-efficient evolutionary exploration of planning alternatives in which successive populations systematically select planning alternatives that reduce cost and increase throughput. This paper presents the architecture of the coevolutionary virtual design environment, and examines the network-based performance of the coevolutionary algorithms that execute in this environment. Simulation analysis examines the percentage convergence error and percentage computational advantage comparing the distributed network-based implementation to a centralized network-based implementation. The algorithms and architectures are evaluated in a realistic network setting and analyzed using models of network delays and processing times.

关键词： coevolutionary algorithms coevolutionary virtual design environment design and manufacturing multiagent architecture network-based distributed planning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Fitness Predictors in coevolutionary Cartesian Genetic Programming

引用

EVOLUTIONARY COMPUTATION 2019年第3期27卷 497-523页

作者： Drahosova, Michaela Sekanina, Lukas Wiglasz, Michal Brno Univ Technol Fac Informat Technol IT4Innovat Ctr Excellence Bozetechova 2 Brno 61266 Czech Republic

In genetic programming (GP), computer programs are often coevolved with training data subsets that are known as fitness predictors. In order to maximize performance of GP, it is important to find the most suitable parameters of coevolution, particularly the fitness predictor size. This is a very time-consuming process as the predictor size depends on a given application, and many experiments have to be performed to find its suitable size. A new method is proposed which enables us to automatically adapt the predictor and its size for a given problem and thus to reduce not only the time of evolution, but also the time needed to tune the evolutionary algorithm. The method was implemented in the context of Cartesian genetic programming and evaluated using five symbolic regression problems and three image filter design problems. In comparison with three different CGP implementations, the time required by CGP search was reduced while the quality of results remained unaffected.

关键词： Cartesian genetic programming coevolutionary algorithms fitness prediction symbolic regression evolutionary design image processing

来源：评论

学校读者我要写书评

暂无评论

Competitive coevolutionary Learning of Fuzzy Systems for Job Exchange in Computational Grids

引用

EVOLUTIONARY COMPUTATION 2009年第4期17卷 545-560页

作者： Foelling, Alexander Grimme, Christian Lepping, Joachim Papaspyrou, Alexander Schwiegelshohn, Uwe Tech Univ Dortmund Sect Informat Technol Robot Res Inst D-44227 Dortmund Germany

In our work, we address the problem of workload distribution within a computational grid. In this scenario, users submit jobs to local high performance computing (HPC) systems which are, in turn, interconnected such that the exchange of jobs to other sites becomes possible. Providers are able to avoid local execution of jobs by offering them to other HPC sites. In our implementation, this distribution decision is made by a fuzzy system controller whose parameters can be adjusted to establish different exchange behaviors. In such a system, it is essential that HPC sites can only benefit if the workload is equitably (not necessarily equally) portioned among all participants. However, each site egoistically strives only for the minimization of its own jobs' response times regularly at the expense of other sites. This scenario is particularly suited for the application of a competitive coevolutionary algorithm: the fuzzy systems of the participating HPC sites are modeled as species that evolve in different populations while having to compete within the commonly shared ecosystem. Using real workload traces and grid setups, we show that opportunistic cooperation leads to significant improvements for each HPC site as well as for the overall system.

关键词： coevolutionary algorithms genetic fuzzy systems grid scheduling

来源：评论

学校读者我要写书评

暂无评论

coevolutionary-based mechanisms for network anomaly detection

引用

Journal of Mathematical Modelling and algorithms 2007年第3期6卷 411-431页

作者： Ostaszewski, Marek Seredynski, Franciszek Bouvry, Pascal Faculty of Sciences Technology and Communication University of Luxembourg Luxembourg-Kirchberg 1359 6 rue Coudenhove Kalergi Luxembourg Polish-Japanese Institute of Information Technology Warsaw 02-008 Koszykowa 86 Poland Institute of Computer Science Polish Academy of Sciences Warsaw 01-237 Ordona 21 Poland

The paper presents an approach based on the principles of immune systems applied to the anomaly detection problem. Flexibility and efficiency of the anomaly detection system are achieved by building a model of the network behavior based on the self-nonself space paradigm. Covering both self and nonself spaces by hyperrectangular structures is proposed. The structures corresponding to self-space are built using a training set from this space. The hyperrectangular detectors covering nonself space are created using a niching genetic algorithm. A coevolutionary algorithm is proposed to enhance this process. The results of experiments show a high quality of intrusion detection, which outperform the quality of the recently proposed approach based on a hypersphere representation of the self-space. © 2007 Springer Science + Business Media B.V.

关键词： Anomaly detection problem Artificial immune systems coevolutionary algorithms Computer networks Hyperrectangular detectors Self-nonself space paradigm

来源：评论

学校读者我要写书评

暂无评论

coevolutionary Deep Reinforcement Learning

Coevolutionary Deep Reinforcement Learning

引用

IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Cotton, David Traish, Jason Chaczko, Zenon Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW Australia

ISBN: (纸本)9781728125473

The ability to learn without instruction is a powerful enabler for learning systems. A mechanism for this, self-play, allows reinforcement learning to develop high performing policies without large datasets or expert knowledge. Despite these benefits, self-play is known to be less sample efficient and suffer unstable learning dynamics. This is in part due to a non-stationary learning problem where an agent's actions influence their opponents and as a consequence the training data they receive. In this paper we demonstrate that competitive pressures can be utilised to improve self-play. This paper leverages coevolution, an evolutionary inspired process in which individuals are compelled to innovate and adapt, to optimise the training of a population of reinforcement learning agents. We demonstrate that our algorithm improves the final performance of a Rainbow DQN trained in the game Connect Four, achieving a 15% higher win percentage over the next leading self-play algorithm. Furthermore, our algorithm exhibits more stable training with less variation in evaluation performance.

关键词： Deep reinforcement learning Multi-agent systems coevolutionary algorithms Population based training Self-play

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：