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Knowledge management overview of feature selection problem in high-dimensional financial data: cooperative co-evolution and MapReduce perspectives
2019
Problems and Perspectives in Management
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This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-the-art cooperative co-evolution and MapReduce-based feature selection techniques, and future ...
Cooperative co-evolution, a meta-heuristic algorithm and a divide-and-conquer approach, decomposes high-dimensional problems into smaller sub-problems. ...
A self-adaptive differential evolution (DE)
approach (A. Ghosh, Datta, & S. ...
doi:10.21511/ppm.17(4).2019.28
fatcat:76yr472o6rf7vm3torvgnxfcnm
A Survey on Metaheuristics for Solving Large Scale Optimization Problems
2017
International Journal of Computer Applications
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In the research community, they are generally labeled as Large Scale Global Optimization (LSGO) problems. Several Metaheuristics has been proposed to tackle these problems. ...
Broadly these algorithms can be categorized in 3 groups: Standard Evolutionary Algorithms, Cooperative Co-evolution (CC) based Evolutionary Algorithms and Memetic Algorithms. ...
-Removing Adaptive weighting strategy for co-adaption. -Self-adaption of sub-component sizes. ...
doi:10.5120/ijca2017914839
fatcat:2lhciqf4lbgetpyeouf5xykps4
pSum-SaDE: A Modifiedp-Median Problem and Self-Adaptive Differential Evolution Algorithm for Text Summarization
2011
Applied Computational Intelligence and Soft Computing
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To solve the optimization problem a self-adaptive differential evolution algorithm is created. ...
In the paper is proposed a self-adaptive scaling factor in original DE to increase the exploration and exploitation ability. ...
Their insight and comments led to a better presentation of the ideas expressed in this paper. ...
doi:10.1155/2011/351498
fatcat:knxw7xqnjva2hcbnlucdxza7hq
Multi-variant differential evolution algorithm for feature selection
2020
Scientific Reports
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The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique. ...
This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository ...
Self-adaptive scaling factor. Scaling factor F has substantial effect on the convergence speed as a favorable control parameter. ...
doi:10.1038/s41598-020-74228-0
pmid:33057120
fatcat:s5cjfteq6jb2jk2pjyju57qnuu
Feature Selection for Text and Image Data Using Differential Evolution with SVM and Naïve Bayes Classifiers
2020
Engineering Journal
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The proposed approach uses differential evolution (DE) for feature selection with naïve bayes (NB) and support vector machine (SVM) classifiers to enhance the performance of selected classifier. ...
To optimize such challenges, a hybrid approach is suggested in this paper. ...
In the proposed scheme a new self-adaptive mutation approach is proposed which can improve the convergence speed. ...
doi:10.4186/ej.2020.24.5.161
fatcat:k5zjg45xubatnpyzfhv46lsi7i
Cooperative Co-evolution for large scale optimization through more frequent random grouping
2010
IEEE Congress on Evolutionary Computation
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Finally we propose a new technique for self-adaptation of the subcomponent sizes in CC. ...
It also uses another technique called adaptive weighting for co-adaptation of subcomponents. ...
EP/G002339/1) on "Cooperatively Coevolving Particle Swarms for Large Scale Optimisation". ...
doi:10.1109/cec.2010.5586127
dblp:conf/cec/OmidvarLYY10
fatcat:fjerlp3vwrdbveshxhkizkeqni
A Novel Method for Grouping Variables in Cooperative Coevolution for Large-scale Global Optimization Problems
2018
Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics
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We have implemented the proposed approach in a new evolutionary algorithm (DECC-RAG), which uses the Self-adaptive Differential Evolution (DE) with Neighborhood Search (SaNSDE) as the core search technique ...
Large-scale global optimization (LSGO) is known as one of the most challenging problem for evolutionary algorithms (EA). ...
RELATED WORK
Classical Differential and Self-Adaptive Differential Evolution with Neighborhood Search (SaNSDE) Differential evolution (DE) is one the most popular and efficient evolutionary algorithm ...
doi:10.5220/0006903102710278
dblp:conf/icinco/VakhninS18
fatcat:ehxofkfh3nbtnev5g4ronfxhhu
Solution of Mixed-Integer Optimization Problems in Bioinformatics with Differential Evolution Method
2021
Mathematics
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A wide range of methods has been developed for its solution, including metaheuristics approaches. ...
The method was also successfully used to optimize the training set of samples for such a genomic selection model. ...
In [26] the authors developed differential evolution with a binary mutation scheme for feature selection. A novel adapted mixed-integer differential evolution algorithm was designed in [27] . ...
doi:10.3390/math9243329
fatcat:4ovnoxyr7rbfvmhbg25lgks77i
Quantitative Imaging in Cancer Evolution and Ecology
2013
Radiology
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These evolutionary dynamics are both a cause and a consequence of cancer system heterogeneity at many scales, ranging from genetic properties of individual cells to large-scale imaging features. ...
However, most current quantitative metrics lack spatialness, expressing quantitative radiologic features as a single value for a region of interest encompassing the whole tumor. ...
Rather than a single self-organized system, cancers represent a patchwork of habitats, each with a unique set of environmental selection forces and cellular evolution strategies. ...
doi:10.1148/radiol.13122697
pmid:24062559
pmcid:PMC3781355
fatcat:v62ufiewqnhw7ktiowvvrpcjz4
Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity
2018
Turkish Journal of Electrical Engineering and Computer Sciences
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Consequently, this paper proposes a ranking self-adaptive differential evolution (rsaDE) feature selection algorithm. ...
The proposed algorithm is capable of selecting the optimal feature subsets while improving the recognition of acceleration activity using a minimum number of features. ...
Afterward, the highly ranked features are evaluated by using the self-adaptive differential evolution algorithm. ...
doi:10.3906/elk-1709-138
fatcat:louyr6kvqfghroqx7uqmxdu434
Observing the Evolution of Neural Networks Learning to Play the Game of Othello
2005
IEEE Transactions on Evolutionary Computation
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Success in this case was due to a simple spatial preprocessing layer in the neural network that captured spatial information, self-adaptation of every weight and bias of the neural network, and a selection ...
A study was conducted to find out how game-playing strategies for Othello (also known as reversi) can be learned without expert knowledge. ...
resources for some of the experiments. ...
doi:10.1109/tevc.2005.843750
fatcat:6yyun5j34fe7bcwdilqbgn4264
Limited evaluation cooperative co-evolutionary differential evolution for large-scale neuroevolution
2018
Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '18
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Many real-world control and classification tasks involve a large number of features. ...
First, classic evolutionary algorithms tend not to scale well for searching large parameter spaces; second, the network evaluation over a large number of training instances is in general time-consuming ...
The indirect NE methods can help scaling evolutionary approaches for evolving large networks. ...
doi:10.1145/3205455.3205555
dblp:conf/gecco/YamanMIFP18
fatcat:tubqi4m3azh3nkvt3eqqokh43y
Differential evolution for discrete optimization: An experimental study on Combinatorial Auction problems
2008
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
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In this paper, we use JADE, a self-adaptive DE algorithm, for winner determination in Combinatorial Auctions (CAs) where users place bids on combinations of items. ...
Differential evolutionary (DE) mutates solution vectors by the weighted difference of other vectors using arithmetic operations. ...
As a comparison, parameter selection for JADE is very simple owing to the ability of parameter self-adaptation. ...
doi:10.1109/cec.2008.4631173
dblp:conf/cec/ZhangASM08
fatcat:huknwcpx7zfz3ok767n2owfumq
Towards Crafting a Smooth and Accurate Functional Link Artificial Neural Networks Based on Differential Evolution and Feature Selection for Noisy Database
2015
International Journal of Computational Intelligence Systems
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The accuracy and smoothness of the network is taken birth by suitably tuning the parameters of FLANN using differential evolution and filter based feature selection. ...
In specific, the differential evolution is used to fine tune the weight vector of this network and some trigonometric functions are used in functional expansion unit. ...
is that it is self-scaling. ...
doi:10.1080/18756891.2015.1036221
fatcat:unag5hfvlndrlhsmoiktfq3tli
Differential Evolution: A Survey and Analysis
2018
Applied Sciences
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In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. ...
Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. ...
Self-Adaptive Differential Evolution (SaDE) Self-adaptive differential evolution (SaDE) is simultaneously applied to a pair of mutation techniques "DE/rand/1" and "DE/current-to-best/2" [52] . ...
doi:10.3390/app8101945
fatcat:2dmgbczh5zf4jjwoqcwevsvbli
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