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Adaptive evolutionary programming with p-best mutation strategy

SWARM AND evolutionary COMPUTATION 2013年 9卷 58-68页

作者： Das, Swagatam Mallipeddi, Rammohan Maity, Dipankar Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 W Bengal India Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Although initially conceived for evolving finite state machines, evolutionary programming (EP), in its present form, is largely used as a powerful real parameter optimizer. For function optimization. EP mainly relies on its mutation operators. Over past few years several mutation operators have been proposed to improve the performance of EP on a wide variety of numerical benchmarks. However, unlike real-coded GAs, there has been no fitness-induced bias in parent selection for mutation in EP. That means the i-th population member is selected deterministically for mutation and creation of the i-th offspring in each generation. In this article we propose a p-best mutation scheme for EP where any one from the p (p is an element of [1,2, ... ,mu], where mu denotes population size) top-ranked population-members (according to fitness values) is selected randomly for mutation. The scheme is invoked with 50% probability with each index in the current population, i.e. the i-th offspring can now be obtained either by mutating the i-th parent or by mutating a randomly selected individual from the p top-ranked vectors. The percentage of best members is made dynamic by decreasing p in from mu/2 to 1 with generations to favor explorative behavior at the early stages of search and exploitation during the later stages. We investigate the effectiveness of introducing controlled bias in parent selection in conjunction with an Adaptive Fast EP (AFEP), where the value of a strategy parameter is updated based on the previous records of successful mutations by the same parameter. Comparison with the recent and best-known versions of EP over 25 benchmark functions from the CEC (Congress on evolutionary Computation) 2005 test-suite for real-parameter optimization and two other engineering optimization problems reflects the statistically validated superiority of the new scheme in terms of final accuracy, speed, and robustness. Comparison with AFEP without p-best mutation demonstrates the imp

关键词： evolutionary programming Real-parameter optimization Mutation Parent selection evolutionary algorithms

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Neural network design based on evolutionary programming

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ARTIFICIAL INTELLIGENCE IN ENGINEERING 1997年第2期11卷 155-161页

作者： Fang, J Xi, YG Institute of Automation Shanghai Jiao Tong University Shanghai 200030 People's Republic of China

In this paper, a new design method for neural networks is presented based on evolutionary programming. By using an evolutionary algorithm the structure and weights of static neural networks can be simultaneously acquired. This method is further extended to design recurrent neural networks through introducing 'delayed links' into networks. Simulation results are also given to illustrate the efficiency of the proposed method. (C) 1997 Elsevier Science Ltd.

关键词： evolutionary programming connection rules neural network mutation

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Optimization of a Linear Fresnel Reflector Applying Computational Fluid Dynamics, Entropy Generation Rate and evolutionary programming

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RENEWABLE ENERGY 2020年 152卷 698-712页

作者： Lopez-Nunez, Oscar A. Arturo Alfaro-Ayala, J. Ramirez-Minguela, J. J. Belman-Flores, J. M. Jaramillo, O. A. Univ Guanajuato DCNE Dept Chem Engn Col Noria Alta S-N Guanajuato 36050 Gto Mexico Univ Guanajuato DICIS Dept Mech Engn Salamanca Valle de Santiago Km 3-5 1-8 Salamanca 36885 Gto Mexico Univ Nacl Autonoma Mexico Inst Energias Renovables Priv Xochicalco S-N Temixco 62580 Morelos Mexico

This work presents an optimization of a Linear Fresnel Reflector based on the Computational Fluid Dynamics, the Entropy Generation Rate and the evolutionary programming method. The objective function of the optimization process takes into account the maximization of the absorbed radiation solar flux on the receiver tube and the minimization of the total Entropy Generation Rate. A set of design equations were used to build the Linear Fresnel Reflector geometries of each one of the individuals per generation. The design equations consider, among others, a coupling between the angles and distances of the mirrors and the required geometrical parameters for the construction of the CPC secondary reflector. The evolutionary programming considers a small population of six individuals per generation and takes into account a search space for geometric parameters such as the aperture area, the width and the length of the mirrors. The mutation operator is applied to generate the individuals and the selection operator is applied to find the best individuals for the next generation. Seven generations were needed to find the optimal Linear Fresnel Reflector. The optimal Linear Fresnel Reflector (NN individual) presents an increase of 2.48% for the average absorbed radiation flux on the absorber tube and a decrease of 20% for the total Entropy Generation Rate, both in comparison with a prototype of a Linear Fresnel Reflector. For the absorbed radiation flux, both individuals presents the minimum values on the top side of the absorber tube (1,386 W m-2 and 1,982 W m-2 for the prototype and NN individual respectively), while the maximum values are located at the lower part of the absorber tube (7,180 W m-2 and 8,199 W m-2 for the prototype and NN individual respectively). In terms of local Entropy Generation Rate, the NN individual has a decrease of 14.6%, 60% and 36.8% of Entropy Generation Rate due to viscous dissipation, heat transfer and radiation respectively at the CPC zone in

关键词： Linear fresnel reflector Optimization Entropy generation evolutionary programming Computational fluid dynamics

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Exergoeconomic analysis and optimization of a cogeneration system using evolutionary programming

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APPLIED THERMAL ENGINEERING 2008年第13期28卷 1580-1588页

作者： Sahoo, P. K. Indian Inst Technol Roorkee Dept Mech & Ind Engn Roorkee Uttar Pradesh India

Exergoeconomic analysis helps designers to find ways to improve the performance of a system in a cost effective way. Most of the conventional exergoeconomic optimisation methods are iterative in nature and require the interpretation of the designer at each iteration. In this work, a cogeneration system that produces 50 MW of electricity and 15 kg/s of saturated steam at 2.5 bar is optimized using exergoeconomic principles and evolutionary programming. The analysis shows that the product cost, cost of electricity and steam, is 9.9% lower with respect to the base case. This is achieved, however, with 10% increase in capital investment. Moreover, it is important to note that the additional investment can be paid back in 3.23 years. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： exergy exergoeconomics optimization evolutionary programming cogeneration

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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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Optimal radial distribution system reconfiguration using fuzzy adaptation of evolutionary programming

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2003年第10期25卷 775-780页

作者： Venkatesh, B Ranjan, R Multimedia Univ Fac Engn & Technol Bukit Beruang 75450 Melaka Malaysia

Optimal reconfiguration of Radial Distribution System (RDS) is done under the umbrella of Supervisory Control and Data Acquisition (SCADA) systems to achieve the best voltage profile and minimal kW losses amongst several objectives. This problem requires the determination of the best combination of feeders from each loop in the RDS to be switched out such that the resulting RDS gives the optimal performance in the chosen circumstance. The problem has a discontinuous solution space and certain problem variables assume discrete values of zero or one. This paper proposes a method that uses fuzzy adaptation of evolutionary programming (FEP) as a solution technique. FEP technique has been chosen as it is particularly suited while solving optimization problems with discontinuous solution space and when the global optimum is desired. Fuzzy adaptation of EP is necessitated while considering optimization of multiple objectives. The proposed method is tested on established RDS and results are presented. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： radial distribution systems evolutionary programming tie-switches

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Distribution system planning with evolutionary programming and a reliability cost model

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IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 2000年第6期147卷 336-341页

作者： Lin, WM Yang, CD Tsay, MT Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 80424 Taiwan Cheng Shiu Inst Technol Dept Elect Engn Kaohsiung Taiwan

Cost of reliability of optimal distribution system planning is considered. The reliability cost model has been derived as a linear function of line no flows for evaluating the outages. The objective is to minimise the total cost including the outage cost, feeder resistive loss, and fixed investment cost. evolutionary programming was used to solve the very complicated mixed-integer, highly nonlinear and nondifferential problem. A real distribution network was modelled as the sample system for tests. There is also a higher opportunity to obtain the global optimum during the EP process.

关键词： feeder resistive loss power distribution reliability Reliability optimal distribution system planning Optimisation techniques Optimisation highly nonlinear problem evolutionary programming total cost minimisation evolutionary computation reliability cost model nondifferential problem Distribution networks mixed-integer problem fixed investment cost distribution system planning outages evaluation Maintenance and reliability Power system planning and layout linear function power distribution planning line flows

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A clustering algorithm using an evolutionary programming-based approach

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PATTERN RECOGNITION LETTERS 1997年第10期18卷 975-986页

作者： Sarkar, M Yegnanarayana, B Khemani, D Indian Inst Technol Dept Comp Sci & Engn Madras 600036 Tamil Nadu India

In this paper, an evolutionary programming-based clustering algorithm is proposed. The algorithm effectively groups a given set of data into an optimum number of clusters. The proposed method is applicable for clustering tasks where clusters are crisp and spherical. This algorithm determines the number of clusters and the cluster centers in such a way that locally optimal solutions are avoided. The result of the algorithm does not depend critically on the choice of the initial cluster centers. (C) 1997 Published by Elsevier Science B.V.

关键词： clustering K-means optimization evolutionary programming

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Hydro-thermal unit commitment problem using simulated annealing embedded evolutionary programming approach

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2011年第4期33卷 939-946页

作者： Rajan, C. Christober Asir Pondicherry Engn Coll Dept EEE Pondicherry 605014 India

This paper presents a new approach to solve the hydro-thermal unit commitment problem using Simulated Annealing embedded evolutionary programming approach. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. A utility power system with 11 generating units in India demonstrates the effectiveness of the proposed approach;extensive studies have also been performed for different IEEE test systems consist of 25, 44 and 65 units. Numerical results are shown comparing the cost solutions and computation time obtained by conventional methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Hydro-thermal unit commitment evolutionary programming Simulated annealing

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Multiuser detection ford DS-CDMA systems using evolutionary programming

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IEEE COMMUNICATIONS LETTERS 2003年第3期7卷 101-103页

作者： Lim, HS Rao, MVC Tan, AWC Chuah, HI Multimedia Univ Fac Engn & Technol Jalan Ayer Keroh Lama 75450 Melaka Malaysia

This letter introduces a new multiuser detection scheme which uses evolutionary programming (EP) to detect the user bits based on the maximum-likelihood decision rule. The major advantage of the proposed detector is that it has a lower computational complexity compared to other popular evolutionary-algorithm-based detectors. The simulation results show that the EP has always converged to the optimum solution with a small number of generations. The simulated average computational time performance demonstrates that this approach achieves practical ML performance with polynomial complexity in the number of users.

关键词： code-division multiple access (CDMA) evolutionary programming multiuser detection

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