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Algorithms for CAD Tools VLSI Design [chapter]

K.A. Sumithra
2012 VLSI Design  
In order to optimize above mentioned issues this chapter concentrates on different methodologies starting with Memetic Approach in comparison with genetic concept, Neuro-Memetic approach in comparison  ...  Neuro-memetic model makes it possible to predict the sub-circuit from circuit with minimum interconnections between them.  ...  Publisher InTech Published online 20, January, 2012 Published in print edition January, 2012 This book provides some recent advances in design nanometer VLSI chips.  ... 
doi:10.5772/37959 fatcat:gyanbrku5ndgvl6fz2ybfswtvi

Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center

Nico Nico, Novrido Charibaldi, Yuli Fauziah
2022 International Journal of Artificial Intelligence & Robotics (IJAIR)  
From the results of this study, the memetic algorithm is better than the genetic algorithm in making picket schedules.  ...  When it comes to the two algorithms, using genetic algorithms or memetics algorithms may not always offer the optimum outcomes in every situation.  ...  Several algorithms are combined with other algorithms, such as the Neuro-Fuzzy Genetic Algorithm. This algorithm combines a genetic algorithm with ANFIS (Adaptive Neuro-Fuzzy Inference System).  ... 
doi:10.25139/ijair.v4i1.4323 fatcat:fwxrtcqqf5b3vanu5nm5n6sefi

Comparison of principal component analysis and ANFIS to improve EEVE Laboratory energy use prediction performance

Desmira Desmira, Norazhar Abu Bakar, Romi Wiryadinata, Mustofa Abi Hamid, Nur Kholifah, Muhammad Nurtanto
2022 Indonesian Journal of Electrical Engineering and Computer Science  
The model used to achieve this research's goal was called the adaptive neuro-fuzzy inference system (ANFIS) model, which was coupled with principal component analysis (PCA) feature selection.  ...  In conclusion, the ANFIS model coupled with PCA feature selection was excellent at predicting energy needs in the laboratory while the comfort of the students during practicums in the room remained within  ...  Indoor energy consumption models have also been widely used in several studies, one of which is used at Zhejiang University China, namely the genetic algorithm-adaptive neuro-fuzzy inference system (GA-ANFIS  ... 
doi:10.11591/ijeecs.v27.i2.pp970-979 fatcat:gltvtqh6gnhkfcgutnxmgom2iy

Feature selection and classification using flexible neural tree

Yuehui Chen, Ajith Abraham, Bo Yang
2006 Neurocomputing  
This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved.  ...  The FNT structure is developed using genetic programming (GP) and the parameters are optimized by a memetic algorithm (MA).  ...  Introduction Variable selection refers to the problem of selecting input variables that are most predictive for a given outcome.  ... 
doi:10.1016/j.neucom.2006.01.022 fatcat:pp3pqkctfvgrrngq2w5o6aqlee

Prediction of tensile modulus of PA-6 nanocomposites using adaptive neuro-fuzzy inference system learned by the shuffled frog leaping algorithm

Maryam Shahriari-kahkeshi, Mehdi Moghri
2017 E-Polymers  
A multi-input single-output (MISO) ANFIS model is constructed and learned to predict the tensile modulus of PA-6 nanocomposites.  ...  One approach to map the relationship between the process parameters and the tensile modulus of PA-6 nanocomposites is the use of a non-linear system identification tool called the adaptive-neuro fuzzy  ...  Description of ANFIS The adaptive neuro-fuzzy inference system is a fuzzy inference system implemented in the framework of an adaptive NN.  ... 
doi:10.1515/epoly-2016-0235 fatcat:mgyslqnpnvglveepdanvmtfpla

Fuzzy logic based approaches for gene regulatory network inference [article]

Khalid Raza
2018 arXiv   pre-print
The rapid advancement in high-throughput techniques has fueled the generation of large volume of biological data rapidly with low cost.  ...  In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches for GRNI developed during last two decades.  ...  genetic algorithm (dMAG), mutual information based twophase memetic algorithm (MIMA), and memetic algorithm combined with ANN (MANN).  ... 
arXiv:1804.10775v1 fatcat:ct4yxzdq45ebdobxdsuwb3zlmy

Survey on clinical prediction models for diabetes prediction

N. Jayanthi, B. Vijaya Babu, N. Sambasiva Rao
2017 Journal of Big Data  
Selecting model according to situation Depending on situation what model has to be selected is described as for segmentation use clustering algorithm, for developing recommender system use classification  ...  Preparation for establishing clinical prediction models. ii. Dataset selection. iii. Handling variables. iv. Model generation. v. Model evaluation and validation.  ...  Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  ... 
doi:10.1186/s40537-017-0082-7 fatcat:2v4fsoaamzehlfszaatexf3fui

Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs

Cristian Rotaru, Octav Brudaru
2012 2012 IEEE Congress on Evolutionary Computation  
This paper presents a cellular genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The evolution process is inspired by a basic genetic algorithm.  ...  The population evolves on a bidimensional grid and is implicitly organized in geographical clusters that present a form of structural similarity between individuals.  ...  Koczy, Genetic and Bacterial Memetic Programming Approaches in Hierarchical-Interpolative Fuzzy System Construction David J.  ... 
doi:10.1109/cec.2012.6256590 dblp:conf/cec/RotaruB12 fatcat:4ly3nrktw5habc6lf5err7d5py

Time Series Modeling and Forecasting Using Memetic Algorithms for Regime-Switching Models

C. Bergmeir, I. Triguero, D. Molina, J. L. Aznarte, J. M. Benitez
2012 IEEE Transactions on Neural Networks and Learning Systems  
We propose a different fitting procedure, using a memetic algorithm, in order to obtain more accurate models.  ...  The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems.  ...  Using this theory, Medeiros and Veiga [8] developed the neuro-coefficient smooth transition AR (NCSTAR), which uses a neural network to learn the threshold variable as a weighted sum of the inputs from  ... 
doi:10.1109/tnnls.2012.2216898 pmid:24808077 fatcat:k2poyuvkgzev3i3bgcvn3r3l3u

Differential evolution with local information for neuro-fuzzy systems optimisation

Ming-Feng Han, Chin-Teng Lin, Jyh-Yeong Chang
2013 Knowledge-Based Systems  
This paper proposes a differential evolution with local information (DELI) algorithm for Takagi-Sugeno-Kang-type (TSK-type) neuro-fuzzy systems (NFSs) optimisation.  ...  The 1/5th rule dynamically adjusts the tuning scale factor in each period to enhance the search capability of the DELI algorithm.  ...  Many researchers have developed GAs to implement fuzzy systems and neuro-fuzzy systems to automate the determination of parameters and structures [14] [15] [16] [17] [18] [19] [20] [21] [22] .The genetic  ... 
doi:10.1016/j.knosys.2013.01.023 fatcat:37uevfmsuzhu7dh2bz3ejsqymu

Genetics-Based Machine Learning [chapter]

Tim Kovacs
2012 Handbook of Natural Computing  
2 transformations of data based on semantic interpretations of fuzzy sets 3 inherently fuzzy data" p. 558 Genetic Neuro-Fuzzy Systems [198] uses a GA to minimise the error in a FNN [121] uses both  ...  Evolutionary feature selection Some input attributes (features) contribute little or nothing We can simplify and speed learning by selecting only useful ones EAs are widely used in the wrapper approach  ... 
doi:10.1007/978-3-540-92910-9_30 fatcat:rm5bx5lwdvfalolrky6lpyt67a

Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey

Mohammad-H. Tayarani-N., Xin Yao, Hongming Xu
2015 IEEE Transactions on Evolutionary Computation  
algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system.  ...  Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and  ...  Several methods including a genetic fuzzy system and neuro-fuzzy ANFIS were employed.  ... 
doi:10.1109/tevc.2014.2355174 fatcat:le2et3abrjbcbf5jxzgs2gff54

Optimal Classification of Epileptic EEG Signals Using Neural Networks and Harmony Search Methods

Xiao-Zhi Gao, Jing Wang, Jarno M. A. Tanskanen, Rongfang Bie, Xiaolei Wang, Ping Guo, Kai Zenger
2014 Journal of Software  
Index Terms-Harmony Search (HS) method, ElectroEncephaloGram (EEG), BP neural networks, optimization, Opposition-Based Learning (OBL), memetic computing, bee foraging algorithm, signal classification.  ...  It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances.  ...  ACKNOWLEDGMENTS The research work in this paper was supported by the grants from a joint call by the National Natural Science Foundation of China (Project No. 60911130513) and Academy of Finland (Grants  ... 
doi:10.4304/jsw.9.1.230-239 fatcat:ui7x74k3ozblxeuvl4m3p4qsxi

Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis

A. Kheirkhah, A. Azadeh, M. Saberi, A. Azaron, H. Shakouri
2013 Computers & industrial engineering  
Third, it utilizes Principal Component Analysis (PCA) to define input variables versus the trial and process method.  ...  Then, a new algorithm is developed for the time series estimation; in each case an ANN or conventional time series model is selected for the estimation and prediction.  ...  The impact of data pre-processing and post-processing in another method (such as Fuzzy Regression, Genetic Algorithm (GA), Neuro-Fuzzy and Memetic Algorithm) can also be studied. C.6.  ... 
doi:10.1016/j.cie.2012.09.017 fatcat:bqvrbaj3czevzakivxf3m6roee

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

Biaobiao Zhang, Yue Wu, Jiabin Lu, K.-L. Du
2011 Applied Computational Intelligence and Soft Computing  
In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies.  ...  Neural networks and fuzzy systems are two soft-computing paradigms for system modelling.  ...  Acknowledgment This work was supported in part by NSERC.  ... 
doi:10.1155/2011/938240 fatcat:2bgm47lyfva6vi4xhyjtvpmq3a
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