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检索条件"主题词=evolutionary computation"

共 14935 条记录，以下是351-360 订阅

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A New evolutionary computation Framework for Privacy-Preserving Optimization 13

A New Evolutionary Computation Framework for Privacy-Preserv...

引用

13th International Conference on Advanced computational Intelligence (ICACI)

作者： Zhan, Zhi-Hui Wu, Sheng-Hao Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China Chaoyang Univ Technol Taichung 41349 Taiwan Victoria Univ Melbourne Vic 8001 Australia

ISBN: (纸本)9781665412544

evolutionary computation (EC) is a kind of advanced computational intelligence (CI) algorithm and advanced artificial intelligence (AI) algorithm. EC algorithms have been widely studied for solving optimization and scheduling problems in various real-world applications, which act as one of the Big Three in CI and AI, together with fuzzy systems and neural networks. Even though EC has been fast developed in recent years, there is an assumption that the algorithm designer can obtain the objective function of the optimization problem so that they can calculate the fitness values of the individuals to follow the "survival of the fittest" principle in natural selection. However, in a real world application scenario, there is a kind of problem that the objective function is privacy so that the algorithm designer can not obtain the fitness values of the individuals directly. This is the privacy-preserving optimization problem (PPOP) where the assumption of available objective function does not check out. How to solve the PPOP is a new emerging frontier with seldom study but is also a challenging research topic in the EC community. This paper proposes a rank-based cryptographic function (RUT) to protect the fitness value information. Especially, the RCF is adopted by the algorithm user to encrypt the fitness values of all the individuals as rank so that the algorithm designer does not know the exact fitness information but only the rank information. Nevertheless, the RCF can protect the privacy of the algorithm user but still can provide sufficient information to the algorithm designer to drive the EC algorithm. We have applied the RCF privacy-preserving method to two typical EC algorithms including particle swarm optimization (PSO) and differential evolution (DE). Experimental results show that the RUE-based privacy-preserving PSO and DE can solve the PPOP without performance loss.

关键词： evolutionary computation computational intelligence artificial intelligence privacy preserving optimization problem rank-based cryptographic function

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A Software Platform for evolutionary computation with Pluggable Parallelism and Quality Assurance

A Software Platform for Evolutionary Computation with Plugga...

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12th INNS EANN-SIG International Conference (EANN 2011)/7th IFIP 12 5 International Conference (AIAI 2011)

作者： Evangelista, Pedro Pinho, Jorge Goncalves, Emanuel Maia, Paulo Sobral, Joao Luis Rocha, Miguel Univ Minho CCTC Dept Informat P-4710057 Braga Portugal

ISBN: (纸本)9783642239601;9783642239595

This paper proposes the Java evolutionary computation Library (JECoLi), an adaptable, flexible, extensible and reliable software framework implementing metaheuristic optimization algorithms, using the Java programming language. JECoLi aims to offer a solution suited for the integration of evolutionary computation (EC)-based approaches in larger applications, and for the rapid and efficient benchmarking of EC algorithms in specific problems. Its main contributions are (i) the implementation of pluggable parallelization modules, independent from the EC algorithms, allowing the programs to adapt. to the available hardware resources in a transparent way, without. changing the base code;(ii) a flexible platform for software quality assurance that allows creating tests for the implemented features and for user-defined extensions. The library is freely available as an open-source project.

关键词： evolutionary computation Open-source software Parallel evolutionary Algorithms Software Quality

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Agent Teams and evolutionary computation: Optimizing Semi-Parametric Spatial Autoregressive Models

Agent Teams and Evolutionary Computation: Optimizing Semi-Pa...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Krisztin, Tamas Koch, Matthias Vienna Univ Business & Econ Inst Econ Geog & GISci A-1090 Vienna Austria

ISBN: (纸本)9781424496365

Classical spatial autoregressive models share the same weakness as the classical linear regression models, namely it is not possible to estimate non-linear relationships between the dependent and independent variables. In the case of classical linear regression a semi-parametric approach can be used to address this issue. Therefore an advanced semi-parametric modelling approach for spatial autoregressive models is introduced. Advanced semi-parametric modelling requires determining the best configuration of independent variable vectors, number of spline-knots and their positions. To solve this combinatorial optimization problem an asynchronous multi-agent system based on genetic-algorithms is utilized. Three teams of agents work each on a subset of the problem and cooperate through sharing their most optimal solutions. Through this system more complex relationships between the dependent and independent variables can be derived. These could be better suited for the possibly non-linear real-world problems faced by applied spatial econometricians.

关键词： Biological system modeling Econometrics Genetic algorithms Multiagent systems Optimization Problem-solving Spline advanced semi-parametric modelling agent teams asynchronous multiagent system autoregressive processes combinatorial optimization problem evolutionary computation genetic algorithm genetic algorithms independent variable vectors linear regression model multi-agent systems regression analysis semiparametric spatial autoregressive model optimization spatial econometrician spline-knots

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ESDL: A Simple Description Language for Population-Based evolutionary computation 11

ESDL: A Simple Description Language for Population-Based Evo...

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13th Annual Genetic and evolutionary computation Conference (GECCO)

作者： Dower, Steve Woodward, Clinton J. Swinburne Univ Technol Melbourne Vic Australia

ISBN: (纸本)9781450305570

A large proportion of publications in the field of evolutionary computation describe algorithm specialisation and experimentation. Algorithms are variously described using text, tables, flowcharts, functions or pseudocode. However, ambiguity that can limit the efficiency of communication is common. evolutionary System Definition Language (ESDL) is a conceptual model and language for describing evolutionary systems efficiently and with reduced ambiguity, including systems with multiple populations and adaptive parameters. ESDL may also be machine-interpreted, allowing algorithms to be tested without requiring a hand-coded implementation, as may already be done using the esec framework. The style is distinct from existing notations used within the field and is easily recognisable. This paper describes the case for ESDL, provides an overview of ESDL and examples of its use.

关键词： Algorithm implementation algorithm description evolutionary computation

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Reinforcement Learning with evolutionary computation to Policy Search for Autonomous Navigation 35

Reinforcement Learning with Evolutionary Computation to Poli...

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35th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Zhang, Chengsi Dong, Lu Sun, Changyin Southeast Univ Sch Automat Nanjing 210096 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9781728176840

Reinforcement learning has good applications for autonomous navigation in unknown and complex environments. Traditional reinforcement learning methods with the actor-critic framework sometimes will fall into a local optimum because of the complexity of the loss function. Meanwhile, evolutionary computation(EC) is a type of black box optimization algorithm, which has good robustness in policy search but lower sampling efficiency. In order to address the challenge, we introduce an algorithm that combines evolutionary computation with reinforcement learning into navigation intuitively. The parameters of actor neural network are listed as individual characteristics. Each individual represents a policy network. At the end of each episode, individuals with higher fitness function value are selected to the next generation. Other individuals update a certain number of steps through the critic network with shared replay buffer and then move into the next generation. Simulation results demonstrate the effectiveness and feasibility of this algorithm on navigation.

关键词： Reinforcement learning Navigation evolutionary computation Fitness function

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Using evolutionary computation in Construction of Orthomorphism

Using Evolutionary Computation in Construction of Orthomorph...

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1st International Conference on Multimedia Information Networking and Security (MINES 2009)

作者： Tong Yan Zhang Huanguo Han Haiqing Wuhan Univ Comp Sch Key Lab Areospace Informat Secur & Trust Comp Minist Educ Wuhan 430072 Peoples R China

ISBN: (纸本)9780769538433

Orthomorphism is a kind of elementary permutation with good cryptographic properties, which can be used for constructing the S-box in block cipher structure. This paper mainly discussed constructing some orthomorphisms with evolutionary computation, and got some orthomorphisms with high nonlinearity and low difference uniformity by evolutionary algorithm in the open documents for the first time.

关键词： block cipher evolutionary computation orthomorphism

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Application of evolutionary computation in ECAD problems

Application of evolutionary computation in ECAD problems

引用

Conference on Applications and Science of Neural Networks, Fuzzy Systems, and evolutionary computation

作者： Lee, DH Hwang, SH Korea Adv Inst Sci & Technol Dept Elect Engn Yusong Gu Taejon 305701 South Korea

ISBN: (纸本)0819429104

Design of modern electronic systems is a complicated task which demands the use of computer-aided design(CAD) tools. Since a lot of problems in ECAD are combinatorial optimization problems, evolutionary computations such as genetic algorithms and evolutionary programming have been widely employeed to solve those problems. We have applied evolutionary computation techniques to serve ECAD problems such as technology mapping, microcode-bit optimization, data path ordering and peak power estimation, where their benefits are well observed. This paper presents experiences and discusses issues in those applications.

关键词： evolutionary computation genetic algorithm evolutionary programming electronic computer-aided design technology mapping microcode-bit optimization data path ordering peak power estimation

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Data Mining in Complex Diseases Using evolutionary computation

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10th International Work-Conference on Artificial Neural Networks (IWANN 2009)

作者： Aguiar, Vanessa Seoane, Jose A. Freire, Ana Munteanu, Cristian R. Univ A Coruna UDC Fac Informat Coruna Spain

ISBN: (纸本)9783642024771

A new algorithm is presented for finding genotype-phenotype association rules from data related to complex diseases. The algorithm was based on Genetic Algorithms, a technique of evolutionary computation. The algorithm was compared to several traditional data mining techniques and it was proved that it obtained similar classification scores but found more rules from the data generated artificially. fit this paper it is assumed that several groups of SNPs have an impact on the predisposition to develop a complex disease like schizophrenia. It is expected to validate this in a short period of time oil real data.

关键词： Association studies SNPs evolutionary computation genetic algorithms data mining

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Fitness function for evolutionary computation applied in dynamic object simulation and positioning

Fitness function for evolutionary computation applied in dyn...

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IEEE Symposium on computational Intelligence in Vehicles and Transportation Systems (CIVTS)

作者： Wozniak, Marcin Silesian Tech Univ Inst Math Ul Kaszubska 23 PL-44100 Gliwice Poland

ISBN: (纸本)9781479944972

In the paper an idea to apply evolutionary computation method with dedicated fitness function in dynamic system simulation and positioning is presented. Dedicated evolutionary system's efficiency in simulation, optimization and positioning of examined object is discussed. Presented experiments show common duty as well as extensive, overloading and dangerous situations at work. Research results are presented to discuss applied method.

关键词： evolutionary computation simulation dynamic object positioning dynamic object simulation fitness function Biological cells computational modeling Equations Mathematical model Suspensions Vehicle dynamics Vehicles evolutionary algorithm cytology Vehicle dynamics computational modeling vehicle Suspensions simulating Mathematical Model Patient Positioning

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Fusing creative operations into evolutionary computation for composition: From a composer's perspective

Fusing creative operations into evolutionary computation for...

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IEEE Congress on evolutionary computation (IEEE CEC)

作者： Chang, Chun-yien Chen, Ying-ping Open Culture Fdn Open Culture Network Humanities & Fine Arts Taipei Taiwan Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781728121536

This paper presents a study on fusing creative operations into evolutionary computation for music composition, as an attempt to provide a collaborative creation environment between human and machine and to investigate into the mind process during music creation. A framework based on the paradigm of evolutionary computation is proposed to incorporate key mechanisms observed in the creation process and implemented to work on evolving abstract musical ideas and thoughts. The developed software framework is released as open source. The collaborative creation process, involving the framework, the composer, and the performer, is presented and discussed, and the results of this study also include a composed music work for unaccompanied cello, played by a renowned cellist.

关键词： music composition collaborative creation creative operation genetic algorithm evolutionary computation

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