A
Arka Ghosh
Researcher at Indian Institute of Engineering Science and Technology, Shibpur
Publications - 26
Citations - 366
Arka Ghosh is an academic researcher from Indian Institute of Engineering Science and Technology, Shibpur. The author has contributed to research in topics: Differential evolution & Feature selection. The author has an hindex of 8, co-authored 19 publications receiving 247 citations. Previous affiliations of Arka Ghosh include Victoria University of Wellington & Indian Statistical Institute.
Papers
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Journal ArticleDOI
Ensemble feature selection using bi-objective genetic algorithm
TL;DR: An ensemble parallel processing bi-objective genetic algorithm based feature selection method is proposed that outperforms that of other state-of-the-art methods in classification accuracy and statistical measures.
Journal ArticleDOI
A switched parameter differential evolution with optional blending crossover for scalable numerical optimization
TL;DR: Three very simple modifications to the basic DE scheme are presented such that its performance can be improved and made scalable for optimizing functions having a real-valued moderate-to-high number of variables (dimensions) while focusing on preservation of the simplicity offered by its algorithmic framework.
Journal ArticleDOI
Reusing the Past Difference Vectors in Differential Evolution—A Simple But Significant Improvement
TL;DR: By archiving the most promising difference vectors from past generations and then reusing them for generating offspring in the subsequent generations, this strategy can be integrated with any classical or advanced DE variant with no serious overhead in time or space complexity.
Book ChapterDOI
A Switched Parameter Differential Evolution for Large Scale Global Optimization – Simpler May Be Better
TL;DR: Two very simple modifications to Differential Evolution (DE) are presented to enhance its performance for the high-dimensional numerical functions while still preserving the simplicity of its algorithmic framework.
Book ChapterDOI
Gene Selection Using Multi-objective Genetic Algorithm Integrating Cellular Automata and Rough Set Theory
TL;DR: A novel feature selection method is proposed based on the multi-objective genetic algorithm which is applied on population generated by non-linear uniform hybrid cellular automata using Kullbak-Leibler divergence method.