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检索条件"主题词=evolution algorithm"

共 124 条记录，以下是71-80 订阅

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A Comparison of the Taguchi Method and evolutionary Optimization in Multivariate Testing

A Comparison of the Taguchi Method and Evolutionary Optimiza...

引用

IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Jiang, Jingbo Legrand, Diego Severn, Robert Miikkulainen, Risto Univ Penn Philadelphia PA 19104 USA Criteo Paris France Evolv Technol San Francisco CA USA Univ Texas Austin Austin TX 78712 USA Cognizant Technol Solut San Francisco CA USA

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169293

Multivariate testing has recently emerged as a promising technique in web interface design. In contrast to the standard A/B testing, multivariate approach aims at evaluating a large number of values in a few key variables systematically. The Taguchi method is a practical implementation of this idea, focusing on orthogonal combinations of values. It is the current state of the art in applications such as Adobe Target. This paper evaluates an alternative method: population-based search, i.e. evolutionary optimization. Its performance is compared to that of the Taguchi method in several simulated conditions, including an orthogonal one designed to favor the Taguchi method, and two realistic conditions with dependences between variables. evolutionary optimization is found to perform significantly better especially in the realistic conditions, suggesting that it forms a good approach for web interface design and other related applications in the future.

关键词： evolution algorithm Taguchi method multivariate testing web interface design

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Particle Filter with Lamarckian Inheritance for Nonlinear Filtering

Particle Filter with Lamarckian Inheritance for Nonlinear Fi...

引用

IEEE Congress on evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Li, Lin Zhang, Chaozhu Li, Zhannan Li, Yun Harbin Engn Univ Dept Informat & Commun Engn Harbin Peoples R China Dongguan Univ Technol Coll Comp Sci & Technol Dongguan 523808 Peoples R China

ISBN: (纸本)9781509006229

The particle filter (PF) offers significant advantages over other nonlinear filters for non-Gaussian systems. However, it suffers from particle degeneracy and impoverishment, which can lead to deteriorated estimation performance. Through analyzing the filtering and Lamarckian evolution processes in this paper, we develop a Lamarckian PF (LPF), based on the assertion that specific characteristics of an organism are inheritable directly by its offspring. The LPF thus uses an overriding operator for the offspring to inherit traits such that the particle impoverishment problem is mitigated and filtering performance improved. Compared with the generic PF and the latest PF improved by differential evolution (DEPF), the LPF approximates posterior distribution more sufficiently. Meanwhile, the LPF weakens the impact of algorithmic parameters. Measured against the DEPF, the LPF reduces the complexity and improves the filtering speed and accuracy at the same time. Experimental results verify the validity and advantages of this new nonlinear filter.

关键词： particle filter evolution algorithm Lamarckian inheritance nonlinear filtering differential evolution

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An Improved NSGAII algorithm Based on Site-Directed Mutagenesis Method for Multi-Objective Optimization

An Improved NSGAII Algorithm Based on Site-Directed Mutagene...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Ren, Qiang Luo, Fei Ding, Weichao Lu, Haifeng East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781728124858

evolutionary algorithms have been greatly utilized in Multi-objective optimization problems. Existing studies on multi-objective evolutional algorithms (MOEAs) rarely consider the evolutionary environment which may influence the selection and mutation of the individuals in the evolutionary process. Therefore, with consideration of the evolutionary environment of human intervention, this paper proposes a novel site-directed mutagenesis method for MOEA to generate offspring based on reinforcement learning. In the evolutionary process, reinforcement learning is utilized to simulate the site-directed mutagenesis of human intervention, where key genes affecting the current status are identified through Q-learning agent in the mutation stage. The mutation stage is further applied in NSGAII and the new algorithms are named as RL-NSGAII. Different benchmark problems are used to verify the performance of the proposed algorithms through a large number of experiments. Compared with NSGAII, RL-NSGAII has advantages in the convergence speed of Pareto front and has outstanding performance in the diversity and stability of the solution set for both the two-objective and multi-objective benchmark problems.

关键词： evolution algorithm Reinforcement Learning Site-directed mutagenesis Multi-objective Problem

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Study on a novel genetic algorithm for the combinatorial optimization problem

Study on a novel genetic algorithm for the combinatorial opt...

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International Conference on Control, Automation and Systems

作者： Dang, Jian-wu Wang, Yang-ping Zhao, Shu-xu Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China

ISBN: (纸本)9788995003879

A genetic algorithm simulating Darwinian evolution is proposed to yield near-optimal solutions to the multiple traveling salesmen problem (MTSP). A new transformation of the N-city M-salesmen MTSP to the standard traveling salesman problem (TSP) is introduced. The transformed problem is represented by a city-position map with (N+M-1)(-cities) and a single fictitious Salesman. Nothing that Darwinian evolution is itself an optimization process;we propose a heuristic algorithm that incorporates the tents of natural selection. The time complexity of this algorithm is equivalent to the fastest sorting scheme. Computer simulations indicate rapid convergence is maintained even with increasing problem complexity. This methodology can be adapted to tackle a host of other combinatorial problems.

关键词： evolution algorithm traveling salesman problem multi-traveling salesman problem time complexity

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Particle Filter Track-Before-Detect algorithm with Lamarckian Inheritance for Improved Dim Target Tracking

Particle Filter Track-Before-Detect Algorithm with Lamarckia...

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IEEE Congress on evolutionary Computation (CEC)

作者： Li, Lin Li, Yun Dongguan Univ Technol Sch Comp Sci & Network Secur Dongguan 523808 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781509046010

Particle filter track-before-detect (PF-TBD) algorithms offer improvements over track-after-detect algorithms in detecting and tracking dim targets. However, it suffers from the particle collapsing problem, which can lead to deteriorated detection and tracking performance. To address this issue, a Lamarckian particle filter track-before-detect (LPF-TBD) algorithm is developed in this paper. In the LPF-TBD, before a TBD resampling process, a particle update strategy is applied, which is based on Lamarckian overriding and elitist operators designed to improve the particle diversity and efficiency. The effectiveness of the LPF-TBD algorithm is demonstrated using a widely adopted experiment on a target with a low signal-to-noise ratio in an image sequence. Compared with the currently-popular multinomial resampling PF-TBD method, the posterior distribution in the LPF-TBD can be more sufficiently approximated by the particles. Test results show that the LPF-TBD offers higher detection and tracking performance, while strengthening the algorithmic efficiency of particle filtering and evolutionary algorithms.

关键词： track-before-detect particle filter evolution algorithm Lamarckian inheritance

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A Jumping Gene GA for Multi-objective Voltage Control

A Jumping Gene GA for Multi-objective Voltage Control

引用

IEEE International Conference on Industrial Technology

作者： Ma, H. M. Man, K. F.

ISBN: (纸本)9781424417056

This paper presents a new flexible voltage control strategy. The optimal voltage control is considered a multi-objective problem. A set of feasible solutions saved as Pareto frontier is searched by the jumping gene EA. Based on the solution set, a multiple criteria decision making technique is used to select a suitable one which is applied as the finial on-line solution under different preferences. This strategy was tested on the New England 39 buses power system.

关键词： voltage control jumping gene multiple criteria decision making evolution algorithm

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Towards Uncertain QoS-aware Service Composition via Multi-objective Optimization 24

Towards Uncertain QoS-aware Service Composition via Multi-ob...

引用

24th IEEE International Conference on Web Services (ICWS)

作者： Niu, Sen Zou, Guobing Gan, Yanglan Xiang, Yang Zhang, Bofeng Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9781538607527

QoS-awareWeb service composition has recently become one of the most challenging research issues. Although much work has been investigated to solve the problem, they mainly focus on certain QoS of Web services, while QoS with uncertainty exposes the most important characteristic in a real and highly dynamic environment on the Internet. In this paper, with the consideration of uncertain service QoS, we model the issue of Web service composition with QoS uncertainty that is translated into a multi-objective optimization problem via uncertain interval number, which can be solved by our proposed approach via an non-deterministic multi-objective evolutionary algorithm using the strategy of decomposition. Large-scale empirical experiments have been conducted on our simulated datasets. The experimental results demonstrate that our proposed approach can effectively and efficiently find an optimum composite service solution set with satisfactory convergence.

关键词： Uncertain QoS Web service composition Multiobjective optimization evolution algorithm

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evolutionary Swarm algorithm Based on evolutionary Swarm Model

Evolutionary Swarm Algorithm Based on Evolutionary Swarm Mod...

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1st WRI Global Congress on Intelligent Systems (GCIS 2009)

作者： Quan, Haiyan Shi, Xinling Kunming Univ Sci & Technol Fac Informat & Automat Kunming 650051 Peoples R China Yunnan Univ Informat Coll Kunming 650091 Peoples R China

ISBN: (纸本)9780769535715

The paper introduces an evolution swarm model that clearly demonstrates many intelligent algorithms. Based on the model, an evolutionary swarm algorithm is designed In this work, the evolutionary swarm algorithm is tested with 5 multivariable benchmark functions. The simulation results show that the algorithm possesses an excellent performance in the global optimization, and can be efficiently employed to solve the multimodal function optimization for the multimodal function with high dimensionality

关键词： swarm algorithm evolution algorithm evolutionary swarm model evolutionary swarm algorithm

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Dual Models to Facilitate Learning of Policy Network

Dual Models to Facilitate Learning of Policy Network

引用

Chinese Automation Congress (CAC)

作者： Mei, Yidong Gao, Yue Li, Shaoyuan Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Dept Automat Shanghai Peoples R China

ISBN: (纸本)9781728176871

Learning to control agents without the prior knowledge of its own kinematic is a challenging problem. Recently neural network architecture models can be utilized for robust nonlinear lilting. However, complex sensor inputs including RGB images and robot joints states bring difficulties to the convergence of the control policy due to large input space and complex network structure. Besides, adding temporal information can bring more perception capabilities to the agent but also increase the number of parameters for policy networks. We present a new method called DualM-Control, which exploits dual models including self model and image model. DualM-Control algorithm can compress high dimensional spatial and temporal sensor inputs into low dimension data, thus making it possible for policy network with a few thousand parameters to evolve with evolution strategy. We test the algorithm on a challenging simulation environment created on gym and the performance exceeds existing approaches.

关键词： evolution algorithm Robot control Image processing Reinforcement Learning

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Genetic algorithms application for components parametric synthesis optimization

Genetic algorithms application for components parametric syn...

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International Conference on Modern Problems of Radio Engineering, Telecommunication and Computer Science

作者： Lazko, Oxana Lviv Polytech Natl Univ UA-79013 Lvov Ukraine

In the paper the specific character of evolution algorithms application for the optimal parametrical synthesis problem solution is presented. The advantages and disadvantages are shown.

ISBN: (纸本)9781424413218

In the paper the specific character of evolution algorithms application for the optimal parametrical synthesis problem solution is presented. The advantages and disadvantages are shown.

关键词： evolution algorithm genetic algorithm objective function

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