Enhanced Approach on Permissible Data Sets Using Swarm and Genetic Intelligence
V Prasad1, G N V Raja Reddy2

1V Prasad, Department of Computer Science & Engineering, GMR Institute of Technology, (Andhra Pradesh), India.
2G N V Raja Reddy, Department of Information Technology, MVGR College of Engineering, (Andhra Pradesh), India.
Manuscript received on 12 February 2019 | Revised Manuscript received on 02 March 2019 | Manuscript Published on 08 June 2019 | PP: 153-159 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E10290275S419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This work focuses on the artificial way of analysing large datasets using genetic and evolutionary algorithms with multiple features i.e., algorithms are embedded with bin packing problems which generates Hybrid particle swarm optimization (HPSO), Multi spatial genetic algorithm (MSGA) which are further applied on a cancer dataset for classification of bins in the datasets. Random population generated by these algorithms, the fitness values, evaluation procedure plays a vital role. The algorithms increase the count of features and prune for obtaining the optimistic values with random machine learning protocols and the comparative analysis as shown in the graphs and tables. The results are analysed and compared to obtain the most suitable and efficient algorithm for the permissible dataset.
Keywords: Evolutionary Computing, Natural Computing, Hybrid Swarm, Multi Spatial & Comparative Analysis.
Scope of the Article: Swarm Intelligence