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Running-time analysis of evolutionary programming based on Lebesgue measure of searching space

NEURAL COMPUTING & APPLICATIONS 2018年第2期30卷 617-626页

作者： Zhang, Yushan Huang, Han Lin, Zhiyong Hao, Zhifeng Hu, Guiwu Guangdong Univ Finance & Econ Sch Math & Stat Guangzhou 510320 Guangdong Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Guangdong Peoples R China Guangdong Polytech Normal Univ Dept Comp Sci Guangzhou 510665 Guangdong Peoples R China Foshan Univ Sch Math & Big Data Foshan 528000 Peoples R China

There have been many studies on the runtime analysis of evolutionary algorithms in discrete optimization, and however, relatively few homologous results have been obtained on continuous optimization, such as evolutionary programming (EP). This paper presents an analysis of the running time (as approximated by the mean first hitting time) of two EP algorithms based on Gaussian and Cauchy mutations, using an absorbing Markov process model. Given a constant variation, we analyze the running-time upper bound of special Gaussian mutation EP and Cauchy mutation EP, respectively. Our analysis shows that the upper bounds are impacted by individual number, problem dimension number, searching range, and the Lebesgue measure of the optimal neighborhood. Furthermore, we provide conditions whereby the mean running time of the considered EPs can be no more than a polynomial of n. The condition is that the Lebesgue measure of the optimal neighborhood is larger than a combinatorial computation of an exponential and the given polynomial of n. In the end, we present a case study on sphere function, and the experiment validates the theoretical result in the case study.

关键词： evolutionary algorithm Running-time analysis evolutionary programming Lebesgue measure Optimal epsilon neighborhood

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A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming

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APPLIED SOFT COMPUTING 2018年 62卷 162-175页

作者： Hong, Libin Drake, John H. Woodward, John R. Ozcan, Ender Univ Nottingham Sch Comp Sci Jubilee CampusWollaton Rd Nottingham NG8 1BB England Queen Mary Univ London Sch Elect Engn & Comp Sci Operat Res Grp Mile End Rd London E1 4NS England Hangzhou Normal Univ 2318 Yuhangtang Rd Hangzhou 311121 Zhejiang Peoples R China

evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy, or Levy probability distribution. In this paper, we use genetic programming to automatically generate mutation operators for an evolutionary programming system, testing the proposed approach over a set of function classes, which represent a source of functions. The empirical results over a set of benchmark function classes illustrate that genetic programming can evolve mutation operators which generalise well from the training set to the test set on each function class. The proposed method is able to outperform existing human designed mutation operators with statistical significance in most cases, with competitive results observed for the rest. (C) 2017 Elsevier B.V. All rights reserved.

关键词： evolutionary programming Genetic programming Automatic design Hyperheuristics Continuous optimisation

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Research on Image Clustering Based on evolutionary programming Algorithm

Research on Image Clustering Based on Evolutionary Programmi...

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2018 International Conference on Mathematics, Modeling, Simulation and Statistics Application（MMSSA2018）

作者： She Liu Shuying Yang Department of computer and Communication Engineering Tianjin University of Technology Key laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology

ISBN: (纸本)9781510880504

evolutionary programming algorithm is applied to the problem of image clustering. The solution of the problem is encoded by the symbol coding, and swarm intelligent model is used to search the solution of the problem. In evolutionary programming algorithm, the global search ability is effectively improved by mutation operator and selection operator. The excellent diversity of solutions is guaranteed by using Gaussian mutation operator, and the complexity of the evolutionary operation is reduced. The simulation experiments show that the proposed algorithm for image clustering is effective and correct.

关键词： evolutionary programming image clustering swarm intelligence

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evolutionary programming-based Simulation of Bilateral Real Power Contracts by Optimal Placement of Flexible AC Transmission System Devices Using Contingency Analysis

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第7期44卷 806-819页

作者： Krishnan, Balamurugan Ramalingam, Muralisachithanandam Vellayutham, Dharmalingam SASTRA Univ Dept Elect & Elect Engn Thanjavur 613401 India Pandian Saraswathi Yadav Engn Coll Sivagangai India

Under a restructuring atmosphere, energy customers and producers generally create various bilateral real power markets. The transmission company usually carries out these bilateral power markets contained by the restrictions allowed through the power network design and working situation. The market operator can misuse the power markets and earn additional revenue. Flexible Alternate Current Transmission System devices are considered to be one such technology that helps in improving the bilateral real power markets. In this work, a new methodology for correct placement of Flexible Alternate Current Transmission System devices in the restructured power markets is proposed. A generalized evolutionary programming technique is projected to find out the optimal bilateral real power markets with and without Flexible Alternate Current Transmission System devices under a restructuring atmosphere. Flexible Alternate Current Transmission System devices, such as the Thyristor-Controlled Series Compensator, Static VAR Compensator, and Unified Power Flow Controller, are employed. Utilization of these devices helps in minimizing the cost and also helps in reducing the total overloads, excess power flow, and severity of overloading. A sample 4-bus system and 24-bus extra high-voltage Indian system are presented as examples to exemplify the use of this formulation to reduce the price of the bilateral power market.

关键词： bilateral market flexible AC transmission system devices evolutionary programming sample 4-bus system 24-bus extra high-voltage real Indian system generation companies distribution companies

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Optimal Allocations of FACTS Controllers for Economic Dispatch using evolutionary programming 21

Optimal Allocations of FACTS Controllers for Economic Dispat...

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21st International Computer Science and Engineering Conference (ICSEC)

作者： Chansareewittaya, Suppakarn Mae Fah Luang Univ Sch Informat Technol Chiang Rai Thailand

ISBN: (纸本)9781538607879

In this paper, FACTS controllers are set to improve economic dispatch problem of electrical power system. In additional, the total transfer capability is used to indicate in parallel that FACTS controllers can be improved performance of power transfer ability in electrical power system. evolutionary programming is used to determine optimal allocations of FACTS controllers. Two types of FACTS controller are set for using. The first type of FACTS controller is UPFC. The second type of FACTS controller is SVC. The optimal allocations are optimal parameter settings and optimal locations. The IEEE 30-bus system is used to demonstrate as test system. Test result shows that minimum, average and maximum fuel cost with FACTS controller are lower than those without FACTS controller. Moreover, total transfer capability with FACTS controller is closed to test result without FACT controller.

关键词： evolutionary programming FACTS controller economic dispatch transfer capability optimization

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Immune Log-Normal evolutionary programming (ILNEP) for Solving Economic Dispatch Problem with Prohibited Operating Zones 4

Immune Log-Normal Evolutionary Programming (ILNEP) for Solvi...

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4th International Conference on Industrial Engineering and Applications (ICIEA)

作者： Mansor, M. H. Musirin, I. Othman, M. M. Univ Tenaga Nas Coll Engn Dept Elect Power Engn Kajang Selangor Malaysia Univ Teknol MARA UiTM Fac Elect Engn Shah Alam Selangor Malaysia

ISBN: (纸本)9781509067756

Economic dispatch is an important issue in electrical power system planning and operation. Its aim is to dispatch electricity to the consumers at minimal economic cost, while satisfying system constraints. Prohibited operating zones is one of the constraints which requires attention. This paper presents an efficient optimization technique termed as Immune Log-Normal evolutionary programming (ILNEP) to solve economic dispatch problem with prohibited operating zones. The proposed ILNEP optimization technique to solve economic dispatch problem with prohibited operating zones has been tested on the IEEE 26-Bus Reliability Test System (RTS). The proposed method has been tested on three case studies with different load conditions, for validation of its effectiveness. The three case studies are lightly loaded, mediumly loaded and heavily loaded. Comparison of results among ILNEP, artificial immune system (AIS) and evolutionary programming (EP) have been made and it revealed that ILNEP outperformed AIS and EP in giving the lowest total production cost.

关键词： immune log-normal evolutionary programming artificial immune system evolutionary programming economic dispatch prohibited operating zones

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Extending standard evolutionary programming with self-adaptive stable distributions

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2016年第2期31卷 114-142页

作者： Carvalho, Leopoldo B. de Oliveira, Pedro P. B. Univ Presbiteriana Mackenzie Posgrad Engn Eletr Rua Consolacao 896 BR-01302907 Sao Paulo SP Brazil Univ Presbiteriana Mackenzie Fac Comp & Informat Rua Consolacao 896 BR-01302907 Sao Paulo SP Brazil

Applications in evolutionary programming have suggested the use of further stable probability distributions, such as Cauchy and Levy, in the random process associated with the mutations, as an alternative to the traditional, also stable, normal distribution. This work goes further along the encouraging results of the latter, by extending them in a self-adaptive way, with algorithms that are in tune with the standard lineage of evolutionary programming. Evaluations that rely upon standard analytical bench-marking functions and comparative performance tests between them were carried out in respect to the baseline defined by the standard evolutionary programming algorithm that relies on normal distribution. Additional comparative studies were made in respect to various self-adaptive approaches, also proposed herein, and a method drawn from the literature. The results lead to numerical and statistical superiority of the more general stable distribution based approach, when compared with the baseline, and is unclear in regard to the method drawn from the literature, possibly due to distinct implementation details.

关键词： evolutionary programming evolutionary computation stable probability distribution Levy distribution adaptive mutation self-adaptation

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Maximization of WSN Life Using Hybrid evolutionary programming

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INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS 2016年第3期23卷 246-256页

作者： Nagarathna, P. Manjula, R. VIT Univ Sch Comp Sci & Engn SCOPE Vellore 632014 Tamil Nadu India

Of all the challenges faced by wireless sensor networks (WSN), extending the lifetime of the network has received the most attention from researchers. This issue is critically important, especially when sensors are deployed to areas where it is practically impossible to charge their batteries, which are their only sources of power. Besides the development and deployment of ultra low-power devices, one effective computational approach is to partition the collection of sensors into several disjoint covers, so that each cover includes all targets, and then, activate the sensors of each cover one at a time.. This maximizes the possible disjoint covers with an available number of sensors and can be treated as a set-K cover problem, which has been proven to be NP-complete. evolutionary programming is a very powerful algorithm that uses mutation as the primary operator for evolution. Hence, mutation defines the quality and time consumed in the final solution computation. We have applied the self adaptive mutation strategy based on hybridization of Gaussian and Cauchy distributions to develop to develop a faster and better solution. One of the limitations associated with the evolutionary process is that it requires definition of the redundancy covers, and therefore, it is difficult to obtain the upper bound of a cover. To solve this problem, a redundancy removal operator that forces the evolution process to find a solution without redundancy is introduced. Through simulations, it is shown that the proposed method maximizes the lifespan of WSNs.

关键词： Wireless sensor network Life enhancement evolutionary programming Gaussian mutation Cauchy mutation

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Efficient Reconstruction of Subsurface Elliptical-Cylindrical Targets Using evolutionary programming

Efficient Reconstruction of Subsurface Elliptical-Cylindrica...

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IEEE Radar Conference (RadarConf)

作者： Hajebi, Maryam Hoorfar, Ahmad Villanova Univ Antenna Res Lab Ctr Adv Commun Villanova PA 19085 USA

ISBN: (纸本)9781509008636

evolutionary programming (EP) optimization technique is proposed for efficient profile reconstruction and imaging of buried dielectric targets of elliptical-cylindrical shape. In particular, the efficiency of EP-based optimization in finding the location, shape, relative permittivity, and tilt-angle of the two dimensional (2-D) buried dielectric elliptical-cylindrical targets is investigated and statistically compared with Particle Swarm Optimization (PSO) method. Numerical results indicate that evolutionary programming method, as its first reported implementation in subsurface imaging, has a significantly better overall performance than PSO and can be used as a simple, yet efficient and robust global optimization technique for the inverse profiling of buried objects.

关键词： GPR evolutionary programming Particle Swarm Optimization buried object imaging

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Optimization of Fuzzy Inference System's Parameter Using evolutionary programming Algorithm For Teacher Certification In Indonesia 4

Optimization of Fuzzy Inference System's Parameter Using Evo...

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4th International Conference on Information and Communication Technology (ICoICT)

作者： Wicaksono, Bogi Nhita, Fhira Murdiansyah, Danang Triantoro Telkom Univ Computat Sci Bandung 40257 Indonesia

ISBN: (纸本)9781467398794

Teacher have an important role in Indonesia's education as professional educator in all grade. Teacher certification held to select teacher who fullfil basic competence as educator. Indonesia used scoring method with several assesment to make the decision about teacher certification. In this research, we applied the alternative method using Fuzzy Inference System (FIS). Hybrid algorithm with evolutionary programming (EP) algorithm was used to optimize the membership function and rules of FIS. The result from hybrid fuzzy-EP is compared with result from scoring method using confusion matrix. Fuzzy hybrid with EP have accuracy is 78.8% with accuracy of testing 85.6%.

关键词： teacher certification hybrid fuzzy evolutionary programming confusion matrix

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