Training Multi-Layer Perceptron Using Harris Hawks Optimization
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
Ieee
Abstract
In this paper, Harris hawks optimization (HHO) algorithm has been proposed as an up-to-date meta-heuristic algorithm for training multi-layer perceptron (MLP). The performance of the HHO-based MLP trainer was tested by employing five standard data sets (XOR, Balloon, Iris, Breast Cancer and Heart). The results were compared with those obtained with the sine cosine algorithm (SCA). Comparative statistical results showed that using HHO algorithm as a trainer is more effective and has a higher rate of classification ability.
Description
Izci, Davut/0000-0001-8359-0875; Ekinci, Serdar/0000-0002-7673-2553; Eker, Erdal/0000-0002-5470-8384
Keywords
Multi-Layer Perceptron (Mlp), Harris Hawks Optimization (Hho), Sine Cosine Algorithm (Sca)
Turkish CoHE Thesis Center URL
WoS Q
N/A
Scopus Q
N/A
Source
2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) -- JUN 26-27, 2020 -- TURKEY
Volume
Issue
Start Page
279
End Page
283