Evolutionary large-scale multi-objective optimization: A survey
Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in
solving various optimization problems, but their performance may deteriorate drastically …
solving various optimization problems, but their performance may deteriorate drastically …
Selecting training sets for support vector machines: a review
Support vector machines (SVMs) are a supervised classifier successfully applied in a
plethora of real-life applications. However, they suffer from the important shortcomings of …
plethora of real-life applications. However, they suffer from the important shortcomings of …
Simulated annealing-based dynamic step shuffled frog leaping algorithm: Optimal performance design and feature selection
The shuffled frog leaping algorithm is a new optimization algorithm proposed to solve the
combinatorial optimization problem, which effectively combines the memetic algorithm …
combinatorial optimization problem, which effectively combines the memetic algorithm …
Solving large-scale multiobjective optimization problems with sparse optimal solutions via unsupervised neural networks
Due to the curse of dimensionality of search space, it is extremely difficult for evolutionary
algorithms to approximate the optimal solutions of large-scale multiobjective optimization …
algorithms to approximate the optimal solutions of large-scale multiobjective optimization …
Information gain directed genetic algorithm wrapper feature selection for credit rating
S Jadhav, H He, K Jenkins - Applied Soft Computing, 2018 - Elsevier
Financial credit scoring is one of the most crucial processes in the finance industry sector to
be able to assess the credit-worthiness of individuals and enterprises. Various statistics …
be able to assess the credit-worthiness of individuals and enterprises. Various statistics …
Hybrid binary coral reefs optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical datasets
C Yan, J Ma, H Luo, A Patel - Chemometrics and Intelligent Laboratory …, 2019 - Elsevier
The last decades have witnessed accumulation in biomedical data. Though they can be
analyzed to enhance assessment of at-risk patients and improve the diagnosis, a major …
analyzed to enhance assessment of at-risk patients and improve the diagnosis, a major …
Non-technical loss analysis and prevention using smart meters
T Ahmad - Renewable and Sustainable Energy Reviews, 2017 - Elsevier
In the countries such as Pakistan, for analyzing the losses and techniques in the power
distribution and for mitigating, are the two active areas of research which is spread globally …
distribution and for mitigating, are the two active areas of research which is spread globally …
An evolutionary multiobjective model and instance selection for support vector machines with pareto-based ensembles
Support vector machines (SVMs) are among the most powerful learning algorithms for
classification tasks. However, these algorithms require a high computational cost during the …
classification tasks. However, these algorithms require a high computational cost during the …
Groutability estimation of grouting processes with cement grouts using differential flower pollination optimized support vector machine
ND Hoang, DT Bui, KW Liao - Applied Soft Computing, 2016 - Elsevier
This research presents a soft computing methodology for groutability estimation of grouting
processes that employ cement grouts. The method integrates a hybrid metaheuristic and the …
processes that employ cement grouts. The method integrates a hybrid metaheuristic and the …
A co-evolutionary algorithm based on sparsity clustering for sparse large-scale multi-objective optimization
Sparse large-scale multi-objective optimization problems (LSMOPs), which are
characterized by high dimensional search space and sparse Pareto optimal solutions, have …
characterized by high dimensional search space and sparse Pareto optimal solutions, have …