检索结果-内蒙古大学图书馆

Elitism-based compact genetic algorithms

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2003年第4期7卷 367-385页

作者： Ahn, CW Ramakrishna, RS Kwang Ju Inst Sci & Technol Dept Informat & Commun Gwang Ju 500712 South Korea

This paper describes two elitism-based compact genetic algorithms (cGAs)-persistent elitist compact genetic algorithm (pe-cGA), and nonpersistent elitist compact genetic algorithm (ne-cGA). The aim is to design efficient compact-type GAs by treating them as estimation of distribution algorithms ( I EDAs) for solving difficult optimization problems without compromising on memory and computation costs. The idea is to deal with issues connected with lack of memory-inherent disadvantage of cGAs-by allowing a selection pressure that is high enough to offset the disruptive effect of uniform crossover. The point is to properly reconcile the cGA with elitism. The pe-cGA finds a near optimal solution (i.e., a winner) that is maintained as long as other solutions (i.e., competitors) generated from probability vectors are no better. It attempts to adaptively alter the selection pressure according to the degree of problem difficulty by employing only the pair-wise tournament selection strategy. Moreover, it incorporates the equivalent model of the (1 + 1) evolution strategy (ES) with self-adaptive mutation. The pe-cGA, apart from providing a high performance, also reveals the hidden connection between EDAs (e.g., cGA) and ESs (e.g., (1 + 1)-ES). On the other hand, the ne-cGA further improves the performance of the pe-cGA by, avoiding strong elitism that may lead to premature convergence. The ne-cGA comes with all the benefits of the pe-cGA. In addition, it maintains genetic diversity as a bonus. This paper also proposes an analytic model for investigating convergence enhancement (i.e., speedup). Experimental results show that the proposed algorithms, ne-cGA in particular, generally exhibit a better quality of solution and a higher rate of convergence for most of the problems than do the existing cGA, sGA, and (1 + 1) -ES. The speedup model has been verified by experiments. The results also show that an adequate alleviation of elitism further improves the solution quality, as we

关键词： compact genetic algorithms elitism genetic diversity selection pressure speedup

来源：评论

学校读者我要写书评

暂无评论

A Novel Robust Load Frequency Controller for a Two Area Interconnected Power System using LMI and compact genetic algorithms

A Novel Robust Load Frequency Controller for a Two Area Inte...

引用

IEEE Region 10 Conference 2009

作者： Koisap, Chamnan Kaitwanidvilai, Somyot King Mongkuts Inst Technol Ladkrabang Fac Engn Dept Elect Engn Bangkok 10520 Thailand King Mongkuts Inst Technol Ladkrabang Ctr Excellence Innovat Energy Syst Bangkok 10520 Thailand

ISBN: (纸本)9781424445462

This paper proposes a new technique for designing a fixed-structure robust load frequency controller for a two area interconnected power system. The proposed technique uses Linear Matrix Inequality (LMI) method to form an initial solution, and then the global search algorithm, compact genetic Algorithm (cGA), is adopted for evaluating the final solution. By combining both techniques, LMI and cGA, a better solution in terms of robust performance can be achieved. Infinity norm from disturbances to states are formulated as the cost function in our optimization. The performance of the designed system is investigated in comparison with the conventional H infinity loop shaping controller, the robust controller designed by LMI method and the reduced order robust controller by Hankel norm model reduction method. As results indicated, stability margin of our proposed controller is better than that of the static controller designed by LMI method and the reduced order robust controller. In addition, order of the proposed controller is much lower than that of conventional robust loop shaping controller, making it easy to implement in practice.

关键词： Fixed-structure robust loop shaping control LMI approach compact genetic algorithms H infinity loop shaping control

来源：评论

学校读者我要写书评

暂无评论

The Influence Analysis of Pseudorandom Number Generators and Low Discrepancy Sequences for the Family of compact genetic algorithms: Search Behavior Research from Outside Causes to Internal Causes 5th

The Influence Analysis of Pseudorandom Number Generators and...

引用

5th International Conference on Electronics, Communications and Networks (CECNet)

作者： Zhang, Hong-Guang Tang, Bi-Hua Liu, Kai-Ming Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Key Lab Work Safety Intelligent Monitorin Beijing 100876 Peoples R China

ISBN: (纸本)9789811007408;9789811007385

According to probability vectors, compact genetic algorithms (CGAs) generate new individuals by using pseudorandom number generators (PRNGs) or low discrepancy sequences (LDSs). These new generated individuals are the only factors which determine the search directions in CGAs. Therefore, we experimentally study the relationship between probability vectors and PRNGs (or LDSs). Moreover, we primarily investigate the influence analysis of PRNGs and LDSs for the effectiveness and efficiency of the family of CGAs by using analysis of variance (ANOVA), success rate and success performance. According to experimental results, we provide conclusive evidence to suggest using PRNGs (or LDSs) for CGAs. In essence, the frameworks of CGAs and the update method of probability vectors of CGAs are the internal causes that determine the performance of CGAs for different PRNGs and LDSs.

关键词： Influence analysis Pseudorandom number generators Low discrepancy sequences compact genetic algorithms Convergence analysis

来源：评论

学校读者我要写书评

暂无评论

Soft-decision Decoding by a compact genetic Algorithm using higher selection pressure

Soft-decision Decoding by a compact Genetic Algorithm using ...

引用

International conference on wireless networks and mobile communications

作者： Berkani, Ahlam Azouaoui, Ahmed Belkasmi, Mostafa Mohammed V Rabat Univ SIME Labo ENSIAS Rabat Morocco Chouaib Doukkali Univ FS El Jadida El Jadida Morocco

ISBN: (纸本)9781467382243

Selection pressure controls the selection of individuals from one population to the next. It gives individuals of higher quality, higher probability of being used to create the next generation, and so for the algorithm to focus on promising regions in the search space. This paper introduces higher selection pressure to soft-decision decoding based on compact genetic algorithms(cGAD), and shows the effectiveness of tournament selection in the performances of the decoder. The effect of tournament size is also studied, and the new decoder based on higher selection pressure outperforms Chase-2 algorithm.

关键词： compact genetic algorithms higher selection pressure soft-decision decoding tournament selection

来源：评论

学校读者我要写书评

暂无评论

Knowledge Sharing in Cooperative compact genetic Algorithm 3

Knowledge Sharing in Cooperative Compact Genetic Algorithm

引用

3rd International Conference on Computer and Communication Systems (ICCCS)

作者： Gateratanakul, Orakanya Chongstitvatana, Prabhas Chulalongkorn Univ Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781538663509

In this study, we present three methods of sharing knowledge between cooperative compact genetic algorithms. The methods exploit the effect of the worse solutions of the two-cooperative compact genetic algorithms to the search space which can prevent premature convergence. The benefit also encourages exploring other areas in solution space which enhance the opportunity to discover the better solutions. The proposed algorithm has a simple structure requires much less execution time than the non-sharing compact genetic algorithm.

关键词： compact genetic algorithms parallelization of the evolutionary algorithm parallel genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

compact Differential Evolution

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2011年第1期15卷 32-54页

作者： Mininno, Ernesto Neri, Ferrante Cupertino, Francesco Naso, David Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla 40700 Finland Acad Finland FI-00501 Helsinki Finland Tech Univ Bari Dept Elect & Elect Engn I-70100 Bari Italy Polytech Inst Bari Dept Elect & Elect Engn I-70126 Bari Italy

This paper proposes the compact differential evolution (cDE) algorithm. cDE, like other compact evolutionary algorithms, does not process a population of solutions but its statistic description which evolves similarly to all the evolutionary algorithms. In addition, cDE employs the mutation and crossover typical of differential evolution (DE) thus reproducing its search logic. Unlike other compact evolutionary algorithms, in cDE, the survivor selection scheme of DE can be straightforwardly encoded. One important feature of the proposed cDE algorithm is the capability of efficiently performing an optimization process despite a limited memory requirement. This fact makes the cDE algorithm suitable for hardware contexts characterized by small computational power such as micro-controllers and commercial robots. In addition, due to its nature cDE uses an implicit randomization of the offspring generation which corrects and improves the DE search logic. An extensive numerical setup has been implemented in order to prove the viability of cDE and test its performance with respect to other modern compact evolutionary algorithms and state-of-the-art population-based DE algorithms. Test results show that cDE outperforms on a regular basis its corresponding population-based DE variant. Experiments have been repeated for four different mutation schemes. In addition cDE outperforms other modern compact algorithms and displays a competitive performance with respect to state-of-the-art population-based algorithms employing a DE logic. Finally, the cDE is applied to a challenging experimental case study regarding the on-line training of a nonlinear neural-network-based controller for a precise positioning system subject to changes of payload. The main peculiarity of this control application is that the control software is not implemented into a computer connected to the control system but directly on the micro-controller. Both numerical results on the test functions and experimental

关键词： Adaptive systems compact genetic algorithms differential evolution (DE) estimation distribution algorithms

来源：评论

学校读者我要写书评

暂无评论

A hybrid heuristic for the traveling salesman problem

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2001年第6期5卷 613-622页

作者： Baraglia, R Hidalgo, JI Perego, R CNR Ist CNUCE I-56010 Pisa Italy Univ Complutense Dept Arquitectura Comp & Automat E-28040 Madrid Spain

The combination of genetic and local search heuristics has been shown to be an effective approach to solving the traveling salesman problem (TSP). This paper describes a new hybrid algorithm that exploits a compact genetic algorithm in order to generate high-quality tours, which are then refined by means of the Lin-Kernighan (LK) local search. Local optima found by the LK local search are in turn exploited by the evolutionary part of the algorithm in order to improve the quality of its simulated population. The results of several experiments conducted on different TSP instances with up to 13509 cities show the efficacy of the symbiosis between the two heuristics.

关键词： compact genetic algorithms hybrid GA Lin-Kernighan algorithm TSP

来源：评论

学校读者我要写书评

暂无评论

A New compact Teaching-Learning-Based Optimization Method

A New Compact Teaching-Learning-Based Optimization Method

引用

10th International Conference on Intelligent Computing (ICIC)

作者： Yang, Zhile Li, Kang Guo, Yuanjun Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT9 5AH Antrim North Ireland

ISBN: (纸本)9783319093390;9783319093383

Population based heuristic optimization techniques, though powerful, are often limited by the memory size of hardware context when implemented on micro-controllers, embedded systems and commercial robots platforms etc. On the other hand, the teaching-learning based optimization algorithm (TLBO) is a recently proposed algorithm of high performance on both constrained and unconstrained optimization problems. In this paper, a new compact teaching-learning based optimization algorithm (cTLBO) is proposed to combine the strength of the original TLBO and reduce the memory requirement through a compact structure that utilizes an adaptive statistic description to replace the process of a population of solutions. Numerical results on test benchmark functions show that the new algorithm does not sacrifice the efficiency within the limited hardware resources.

关键词： compact genetic algorithms Teaching-Learning Based Optimization Estimation Distribution algorithms Normal Distribution

来源：评论

学校读者我要写书评

暂无评论

A parametric fuzzy CMAC model with hybrid evolutionary learning algorithms

引用

JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2007年第1-2期13卷 89-114页

作者： Lee, Chi-Yung Lin, Cheng-Jian Hsu, Yung-Chi Nankai Inst Technol Dept Comp Sci & Informat Engn Nantou 542 Taiwan Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung 413 Taiwan Chaoyang Univ Technol Grad Inst Informat Taichung 413 Taiwan

This paper shows fundamentals and applications of the parametric fuzzy cerebellar model articulation controller (P-FCMAC) model. It resembles a neural structure that derived from the Albus CMAC algorithm and Takagi-Sugeno-Kang parametric fuzzy inference systems. In this paper, we also propose the hybrid of evolutionary learning algorithm (HELA) to tune the P-FCMAC. The proposed HELA combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of hypercube cells and the adjustable parameters in P-FCMAC are designed concurrently by the HELA. In the proposed HELA, individuals of the same length constitute the same group, and there are multiple groups in a population. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. Illustrative examples were conducted to show the performance and applicability of the proposed model and method.

关键词： compact genetic algorithms cerebellar model articulation controller (CMAC) hypercube fuzzy model

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：