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IEEE Congress on Evolutionary Computation

作者： Sandeep Paul Satish Kumar Lotika Singh Department of Physics and Computer Science Dayalbagh Educational Institute Agra 282110 India Department of Physics and Computer Science Dayalbagh Educational Institute Agra 282110 India.

ISBN: (纸本)9781467315104

The automatic simultaneous selection of structure and parameters of an artificial neural networks is an important area of research. Although many variants of evolutionary algorithms (EA) have been successfully applied to this problem, their demanding memory requirements have restricted their application to real world problems, especially embedded applications with memory constraints. In this paper, structure and parameter learning of a neural network using a novel hybrid compact genetic algorithm (HCGA) is proposed. In the HCGA, each string combines real and binary segments together. For a feedforward neural network, the real segment encodes it weights, while the binary segment encodes the presence/absence of a connection of the network. The proposed hybrid compact genetic algorithm (HCGA) has several advantages: low computational cost, controllable weight regularization leading to automatic architecture discovery. The HCGA is tested on two benchmark problems of Ripley’s synthetic 2-class problem and Mackey glass time series prediction problem. Experimental results show that the proposed algorithm exhibits good performance with low computation cost and controllable network structure.

关键词： Novel Hybrid compact genetic algorithm Simultaneous Structure

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A reduced complexity decoder using compact genetic algorithm for linear block codes

A reduced complexity decoder using compact genetic algorithm...

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International Conference on Advanced Communication Systems and Information Security (ACOSIS)

作者： Ahlam Berkani Mostafa Belkasmi ENSIAS Mohamed V University in Rabat Morocco

ISBN: (纸本)9781509062287

The compact genetic algorithm decoder has been introduced in [9] as an efficient decoding method of linear block codes. It requires less storage memory than genetic algorithms based decoders. One of its major weakness is the big number of necessary iterations to reach convergence in comparison with genetic algorithms (GA) based decoders. We propose, in this work, new ideas allowing us to reduce the number of iterations from about 10 5 to just about 10 3 which reduces the complexity of decoding. This, without decreasing the decoding performance. We introduce a new stopping criterion based on the soft weight of the probability vector p, a new initialization method of p and we tried to combine both methods all together. Both performance study and the calculation of the average number of iterations ensure the effectiveness of the proposed decoder.

关键词： initialization compact genetic algorithm iterations complexity stopping criterion probability vector soft weight

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compact genetic algorithms with larger tournament size for Soft-Decision Decoding 15

Compact Genetic Algorithms with larger tournament size for S...

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15th International Conference on Intelligent Systems Design and Applications (ISDA)

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

ISBN: (纸本)9781467387095

The standard compact genetic algorithm uses competition between two individuals for which we calculate the objective function and the winner will be the one who gives the best value of this function. This work presents a new approach of compact genetic algorithm that uses more than just two vectors of competition;it introduces tournament selection with larger size using mutation. We apply this algorithm to solve dual domain soft-decision decoding problem. A performance study of the new decoder will be done and the results will be compared to another compact genetic decoder that uses higher selection pressure with randomly generated individuals.

关键词： compact genetic algorithm dual domain soft decision-decoding higher selection pressure tournament size mutation binary individals

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Reducing errors-in-variables bias in linear regression using compact genetic algorithms

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2015年第16期85卷 3216-3235页

作者： Satman, M. Hakan Diyarbakirlioglu, Erkin Istanbul Univ Dept Econometr Istanbul Turkey Univ Paris Est Creteil Val de Marne IRG IAE Gustave Eiffel Creteil France

A new technique is devised to mitigate the errors-in-variables bias in linear regression. The procedure mimics a 2-stage least squares procedure where an auxiliary regression which generates a better behaved predictor variable is derived. The generated variable is then used as a substitute for the error-prone variable in the first-stage model. The performance of the algorithm is tested by simulation and regression analyses. Simulations suggest the algorithm efficiently captures the additive error term used to contaminate the artificial variables. Regressions provide further credit to the simulations as they clearly show that the compact genetic algorithm-based estimate of the true but unobserved regressor yields considerably better results. These conclusions are robust across different sample sizes and different variance structures imposed on both the measurement error and regression disturbances.

关键词： C13 C63 linear regression compact genetic algorithm measurement error

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Efficient Noisy Optimisation with the Multi-Sample and Sliding Window compact genetic algorithms

Efficient Noisy Optimisation with the Multi-Sample and Slidi...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Lucas, Simon M. Liu, Jialin Perez-Liebana, Diego Queen Mary Univ London London E1 4NS England Univ Essex Colchester CO4 3SQ Essex England

ISBN: (纸本)9781538627266

The compact genetic algorithm is an Estimation of Distribution algorithm for binary optimisation problems. Unlike the standard genetic algorithm, no cross-over or mutation is involved. Instead, the compact genetic algorithm uses a virtual population represented as a probability distribution over the set of binary strings. At each optimisation iteration, exactly two individuals are generated by sampling from the distribution, and compared exactly once to determine a winner and a loser. The probability distribution is then adjusted to increase the likelihood of generating individuals similar to the winner. This paper introduces two straightforward variations of the compact genetic algorithm, each of which leads to a significant improvement in performance. The main idea is to make better use of each fitness evaluation, by ensuring that each evaluated individual is used in multiple win/loss comparisons. The first variation is to sample n > 2 individuals at each iteration to make n(n-1)/2 comparisons. The second variation only samples one individual at each iteration but keeps a sliding history window of previous individuals to compare with. We evaluate the methods on two noisy test problems and show that in each case they significantly outperform the compact genetic algorithm, while maintaining the simplicity of the algorithm.

关键词： compact genetic algorithm evolutionary algorithm estimation of distribution sliding window binary optimisation discrete optimisation

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Efficient Noisy Optimisation with the Multi-Sample and Sliding Window compact genetic algorithms

Efficient Noisy Optimisation with the Multi-Sample and Slidi...

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IEEE Symposium Series on Computational Intelligence

作者： Simon M. Lucas Jialin Liu Diego Perez-Liebana Queen Mary University of London United Kingdom University of Essex United Kingdom

The compact genetic algorithm is an Estimation of Distribution algorithm for binary optimisation problems. Unlike the standard genetic algorithm, no cross-over or mutation is involved. Instead, the compact genetic algorithm uses a virtual population represented as a probability distribution over the set of binary strings. At each optimisation iteration, exactly two individuals are generated by sampling from the distribution, and compared exactly once to determine a winner and a loser. The probability distribution is then adjusted to increase the likelihood of generating individuals similar to the winner. This paper introduces two straightforward variations of the compact genetic algorithm, each of which leads to a significant improvement in performance. The main idea is to make better use of each fitness evaluation, by ensuring that each evaluated individual is used in multiple win/loss comparisons. The first variation is to sample n > 2 individuals at each iteration to make n(n - 1)/2 comparisons. The second variation only samples one individual at each iteration but keeps a sliding history window of previous individuals to compare with. We evaluate the methods on two noisy test problems and show that in each case they significantly outperform the compact genetic algorithm, while maintaining the simplicity of the algorithm.

关键词： compact genetic algorithm Evolutionary algorithm Estimation of distribution Sliding window Binary optimisation Discrete optimisation

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Introducing the Cumulation to the Population Based Incremental Learning and the compact GA to Relax genetic Drift 17

Introducing the Cumulation to the Population Based Increment...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Tanaka, Keigo Akimoto, Youhei Shinshu Univ Grad Sch Sci & Technol 4-17-1 Wakasato Nagano 3808553 Japan Shinshu Univ Fac Engn 4-17-1 Wakasato Nagano 3808553 Japan

ISBN: (纸本)9781450349390

The population based incremental learning (PBIL) and the compact genetic algorithm (cGA) are both Bernoulli distribution based search algorithm for optimization of binary variables. The probability parameter that controls the probability of each bit being one is updated based on the ranking of the population from the current distribution. We often observe some parameters move randomly and tend to converge towards undesired values. Such a behavior is called genetic drive and it happens due to a large variance of the parameter update compared to a small expectation of the parameter update. To prevent genetic drive, the learning rate for the parameter update needs to be sufficiently small, but it results in a slow convergence. In this paper, we propose a mechanism to detect genetic drive and prevent parameters from being updated randomly. For this purpose, we introduce the so-called cumulation that is employed in the covariance matrix adaptation evolution strategy. Experimental results show that the cumulation allows PBIL and cGA to use a greater learning rate for the parameter update, leading to a speed up, especially on functions with high redundancy such as LeadingOnes function.

关键词： Population based incremental learning compact genetic algorithm binary optimization genetic drift cumulation

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A methodology for reconfigurable hardware design based upon evolutionary computation

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MICROPROCESSORS AND MICROSYSTEMS 2004年第7期28卷 363-371页

作者： Fernández, F Hidalgo, JI Lanchares, J Sánchez, JM Univ Complutense Madrid Fac Informat E-28040 Madrid Spain Ctr Univ Merida Merida 06800 Spain

In this paper, we present a methodology for Multi-FPGA systems (MFS) design. MFSs are used for a great variety of applications, including dynamically re-configurable hardware applications, digital circuit emulation, and numerical computation. There are a great variety of boards for MFS implementation. We have employed a set of techniques based on evolutionary algorithms, and we show that they are capable of solving all of the design tasks (partitioning placement and routing). Firstly a hybrid compact genetic algorithm solves the partitioning problem and then genetic programming is used to obtain a solution for the two other tasks. (C) 2004 Elsevier B.V. All rights reserved.

关键词： field programmable gate arrays configurable logic blocks genetic programming compact genetic algorithm

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On the model updating operators in univariate estimation of distribution algorithms

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NATURAL COMPUTING 2016年第2期15卷 335-354页

作者： Bronevich, Andrey G. de Oliveira, Jose Valente Natl Res Univ Higher Sch Econ Myasnitskaya 20 Moscow 101000 Russia Univ Algarve CEOT Campus Gambelas P-8005139 Faro Portugal Univ Algarve FCT Campus Gambelas P-8005139 Faro Portugal

The role of the selection operation-that stochastically discriminate between individuals based on their merit-on the updating of the probability model in univariate estimation of distribution algorithms is investigated. Necessary conditions for an operator to model selection in such a way that it can be used directly for updating the probability model are postulated. A family of such operators that generalize current model updating mechanisms is proposed. A thorough theoretical analysis of these operators is presented, including a study on operator equivalence. A comprehensive set of examples is provided aiming at illustrating key concepts, main results, and their relevance.

关键词： compact genetic algorithm Estimation of distribution algorithms Selection operator Theoretical analysis

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Evolutionary Computation Techniques Applied to Phasor Measurement Unit Design

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2013年第4期24卷 573-583页

作者： Mendes da Silva, Raphael Philipe Botazzo Delbem, Alexandre Claudio Coury, Denis Vinicius Univ Sao Paulo Sao Carlos Sch Engn Dept Elect & Comp Engn Sao Carlos SP Brazil Univ Sao Paulo Inst Math & Comp Sci Dept Comp Sci Sao Carlos SP Brazil

This paper presents a performance comparison of methodologies for phasor estimation in power systems. An intelligent tool, the genetic algorithm, with a variation that uses less computing effort, the compact genetic algorithm, was compared to two traditional methods: the Discrete Fourier Transform and the Phase Locked-Loop filter. The mentioned methodologies were programed in C++ language. A comparison with traditional methods was made based on the IEEE C37.118 standard and results show that this intelligent tool presents a better performance, especially during transient events.

关键词： Phasor measurement units genetic algorithms compact genetic algorithm Discrete Fourier Transform Phase Locked-Loop

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