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Designing multilayer perceptrons using a Guided Saw-tooth evolutionary programming Algorithm

SOFT COMPUTING 2010年第6期14卷 599-613页

作者： Antonio Gutierrez, Pedro Hervas, Cesar Lozano, Manuel Univ Cordoba Dept Numer Anal & Comp Sci E-14071 Cordoba Spain Univ Granada E-18071 Granada Spain

In this paper, a diversity generating mechanism is proposed for an evolutionary programming (EP) algorithm that determines the basic structure of Multilayer Perceptron classifiers and simultaneously estimates the coefficients of the models. We apply a modified version of a saw-tooth diversity enhancement mechanism recently presented for Genetic Algorithms, which uses a variable population size and periodic partial reinitializations of the population in the form of a saw-tooth function. Our improvement on this standard scheme consists of guiding saw-tooth reinitializations by considering the variance of the best individuals in the population. The population restarts are performed when the difference of variance between two consecutive generations is lower than a percentage of the previous variance. From the analysis of the results over ten benchmark datasets, it can be concluded that the computational cost of the EP algorithm with a constant population size is reduced by using the original saw-tooth scheme. Moreover, the guided saw-tooth mechanism involves a significantly lower computer time demand than the original scheme. Finally, both saw-tooth schemes do not involve an accuracy decrease and, in general, they obtain a better or similar precision.

关键词： Artificial neural networks Multilayer perceptrons evolutionary algorithms evolutionary programming Population reinitializations Saw-tooth algorithm

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The Neural Networks Ensembles solving Job Shop Schedule Problem based on evolutionary programming

The Neural Networks Ensembles solving Job Shop Schedule Prob...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Liu Fang Beijing Univ Technol Sch Elect Informat & Control Engn Beijing Peoples R China

ISBN: (纸本)9781424467129

A evolutionary programming is proposed in this paper to automatically design neural networks(NNS) ensembles. Based on negative correlation learning, different individual NNs in the ensemble can learn to subdivide the task and thereby solve it more efficiently and elegantly. At the same time, different individual NNs are always to find the best collaboration connection during the evolutionary process. In addition, the architecture of each NN in the ensemble and the size of the ensemble need not to be predefined. The Neural Networks Ensembles based on evolutionary programming is designed in order to solve Job Shop Schedule Problem. The simulation results show that the proposed method in this paper is valid.

关键词： neural networks ensemble evolutionary programming correlation learning

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Heuristic Search Strategy of evolutionary programming

Heuristic Search Strategy of Evolutionary Programming

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2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR)

作者： Han, Zhi-Ming Liu, Xian-Ping Tang, Miao Changchun Univ Technol Inst Informat & Spreading Engn Changchun 130012 Peoples R China Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China Changchun Univ Technol Coll Software Changchun 130012 Peoples R China

ISBN: (纸本)9781424451944

evolutionary programming (EP) has been successfully applied to many optimization problems in recent years. However, most experimental results of EP have been obtained under low-dimension condition and weren't perfect under high-dimension condition. A new evolutionary programming method which named Heuristic Search Strategy (HSS) was proposed for solving high-dimension optimization problem. It can grasp the information of distribution-status of population by control four parameters of population in the evolution process and adjust the mutation size of individual according to such information. HSS was test by using benchmark functions, the experimental results show that performance of HSS is better than other EP method obviously under high dimension condition.

关键词： evolutionary programming Heuristic Search Strategy optimization

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Design for Self-Organizing Fuzzy Neural Networks Based on Adaptive evolutionary programming

Design for Self-Organizing Fuzzy Neural Networks Based on Ad...

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2nd IEEE International Conference on Advanced Computer Control

作者： Liu Fang Beijing Univ Technol Sch Elect Informat & Control Engn Beijing Peoples R China

ISBN: (纸本)9781424458479

A novel hybrid learning algorithm based on a evolutionary programming to design a growing fuzzy neural network, named self-organizing fuzzy neural network based on evolutionary programming, to implement Takagi-Sugeno(TS) type fuzzy models is proposed in this paper. construct and parameters of the fuzzy neural network is trained by evolutionary algorithms. Simulation results demonstrate that a compact and high performance fuzzy rule base can be constructed. Comprehensive comparisons with other approach show that the proposed approach is superior over other in terms of learning efficiency and performance.

关键词： Fuzzy Neural Networks evolutionary programming fuzzy rule

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A Mixed Strategy for evolutionary programming Based on Local Fitness Landscape

A Mixed Strategy for Evolutionary Programming Based on Local...

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2010 IEEE World Congress on Computational Intelligence

作者： Shen, Liang He, Jun Aberystwyth Univ Dept Comp Sci Aberystwyth Dyfed Wales

ISBN: (纸本)9781424481262

The performance of evolutionary programming (EP) is affected by many factors (e.g. mutation operators and selection strategies). Although the conventional approach with Gaussian mutation operator may be efficient, the initial scale of the whole population can be very large. This may lead to the conventional EP taking too long to reach convergence. To combat this problem, EP has been modified in various ways. In particular, modifications of the mutation operator may significantly improve the performance of EP. However, operators are only efficient within certain fitness landscapes. The mixed strategies have therefore been proposed in order to combine the advantages of different operators. The design of a mixed strategy is currently based on the performance of applying individual operators. Little is directly relevant to the information of local fitness landscapes. This paper presents a modified mixed strategy, which automatically adapts to local fitness landscapes, and implements a training procedure to choose an optimal mixed strategy for a given typical fitness landscape. The proposed algorithm is tested on a suite of 23 benchmark functions, demonstrating the advantages of this work in that it is less likely to be stuck in local optima and has a faster and better convergence.

关键词： Gaussian mutation operator Gaussian processes benchmark functions convergence evolutionary computation evolutionary programming local fitness landscape optimal mixed strategy training procedure

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An Improved evolutionary programming with Voting and Elitist Dispersal Scheme

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1st International Conference on Swarm, evolutionary, and Memetic Computing

作者： Maity, Sayan Gunjan, Kumar Das, Swagatam Jadavpur Univ Dept Elect & Telecommun Engg Kolkata 700032 India

ISBN: (纸本)9783642175626

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 present an improved EP variant called evolutionary programming with Voting and Elitist Dispersal (EPVE). The scheme encompasses a voting process which not only gives importance to best solutions but also consider those solutions which are converging fast. By introducing Elitist Dispersal Scheme we maintain the elitism by keeping the potential solutions intact and other solutions are perturbed accordingly, so that those come out of the local minima. By applying these two techniques we can be able to explore those regions which have not been explored so far that may contain optima. 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 reflects the superiority of the new scheme in terms of final accuracy, speed, and robustness.

关键词： evolutionary programming Optimization Tournament Selection Elitism

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EPMAS: evolutionary programming MULTI-AGENT SYSTEMS

EPMAS: EVOLUTIONARY PROGRAMMING MULTI-AGENT SYSTEMS

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24th European Conference on Modelling and Simulation

作者： Peleteiro, Ana M. Burguillo, Juan C. Oplatkova, Zuzana Zelinka, Ivan Univ Vigo Telemat Engn Dept Vigo 36310 Spain Tomas Bata Univ Zlin Fac Appl Informat Zlin 76001 Czech Republic

ISBN: (纸本)9780956494405

evolutionary programming (EP) seems a promising methodology to automatically find programs to solve new computing challenges. The evolutionary programming techniques use classical genetic operators (selection, crossover and mutation) to automatically generate programs targeted to solve computing problems or specifications. Among the methodologies related with evolutionary programming we can find Genetic programming, Analytic programming and Grammatical Evolution. In this paper we present the evolutionary programming Multi-agent Systems (EPMAS) framework based on Grammatical Evolution (GE) to evolutionary generate Multi-agent systems (MAS) ad-hoc. We also present two case studies in MAS scenarios for applying our EPMAS framework: the predator-prey problem and the Iterative Prisoner's Dilemma.

关键词： evolutionary programming Grammatical Evolution Multi-agent Systems

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Multi Objective evolutionary programming to Solve Environmental Economic Dispatch Problem

Multi Objective Evolutionary Programming to Solve Environmen...

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11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)

作者： Qu, Bo-Yang Suganthan, P. N. Pandi, V. R. Panigrahi, B. K. Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore IIT Delhi Dept Elect Engn New Delhi India

ISBN: (纸本)9781424478132

In this paper, the nonlinear constrained multi-objective environmental economic dispatch (EED) problem is solved using fast multi-objective evolutionary programming (FMOEP). Due to the global warming by fossil fuel, environmental concern becomes more and more important in recent years. The purpose of multi-objective optimization algorithm is minimizing all the different objectives simultaneously and finds the best tradeoff solution for this environmental/economic dispatch problem. In order to evaluate the performance of FMOEP on EED problems, the standard IEEE 30-bus six-generator test system is studied. The performance is compared against NSGAII and a number of results reported in literature. The results show that the FMOEP is effective in solving EED problems.

关键词： Environmental/Economic dispatch Multi objective optimization evolutionary programming constraint handling method

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Ab initio protein structure prediction using GPU computing

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Perspectives in Science 2016年 8卷 645-647页

作者： Sandhya Parasnath Dubey N. Gopalakrishna Kini M. Sathish Kumar S. Balaji Dept. of CSE Manipal Institute of Technology Manipal University India Dept. of ECE Manipal Institute of Technology Manipal University India Dept. of Biotechnology Manipal Institute of Technology Manipal University India

Graphics processing unit (GPU) accelerated computing has pioneered a new direction of research for various combinatorial optimization problems. One such problem which requires huge computation is protein structure prediction (PSP). PSP is NP -complete problem. Computational prediction of protein native structure from its primary amino acid sequence is termed as ab initio PSP problem. Till date, wet lab experiments conducted on PSP indicate that existing methods take lots of experimentation time and expensive. As a consequence, only 1% of the sequence's structures are known. This work presents a parallel programming approach with GPU computing for PSP using 2D triangular hydrophobic-polar (HP) lattice model. The implementation of proposed approach is tested on the set of HP benchmark sequence of a length ranging from 25 to 100. The experimental result shows that the proposed approach has significantly improved the performance of prediction with immense drop in computation time.

关键词： Protein structure prediction Hydrophobic-polar model NP -problem Parallel programming Graphics processing unit evolutionary programming

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Economic Dispatch Using Hybrid Particle Swarm Optimization with Prohibited Operating Zones and Ramp Rate Limit Constraints

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2015年第4期10卷 1441-1452页

作者： Prabakaran, S. Senthilkumar, V. Baskar, G. Anna Univ Dept Elect & Elect Engn Madras 600025 Tamil Nadu India Kingston Engn Coll Vellore Tamil Nadu India

This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the evolutionary programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

关键词： Economic dispatch evolutionary programming Particle swarm optimization Hybrid particle swarm optimization Prohibited operating zones Ramp rate limits

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