• Abhishek Jain
• Suhel Ahamed

Dimensionality reduction is a critical preprocessing step in data analysis, aimed at addressing the curse of dimensionality by reducing the number of features while retaining relevant information. Genetic algorithms (GAs) have emerged as effective tools for optimizing feature selection and extraction in this context. This paper provides a comprehensive review and evaluation of genetic algorithms for dimensionality reduction. It explores the underlying principles, methodologies, applications, strengths, and limitations of GAs in reducing the dimensionality of complex datasets. Furthermore, this work examines the empirical performance of GA-based approaches for EEG domains for meditation type. The paper concludes with a discussion on challenges, future directions, and potential research opportunities in genetic algorithm-based dimensionality reduction techniques.

Optimizing Feature Selection and Extraction: Genetic Algorithms in EEG Analysis for Meditation Type Classification

"Optimizing Feature Selection and Extraction: Genetic Algorithms in EEG Analysis for Meditation Type Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 10, page no.493-497, October-2018, Available :http://www.jetir.org/papers/JETIR1810B22.pdf

"Optimizing Feature Selection and Extraction: Genetic Algorithms in EEG Analysis for Meditation Type Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 10, page no. pp493-497, October-2018, Available at : http://www.jetir.org/papers/JETIR1810B22.pdf