Genetic Algorithm as Quadratic Programming Solver

Nik Ahmad Akram, Niusha Shafiabady, Dino Isa

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper we discuss a new approach to solve Quadratic Programming problem in Support Vector Machine (SVM) using Genetic Algorithm. A new strategy is introduced to reduce the number of generations while retaining the SVM accuracy. The approach is tested using a real-time dataset.The proposed approach takes advantage of the multimodal optimization ability of Genetic Algorithm in addition to the classification characterization of SVM by including GA in SVM training phase. This approach incorporates the exploitation and exploration power of GA with generalization ability of SVM at the same time.The achieved results show that the proposed method has had a good performance.
Original languageEnglish
Pages (from-to)1–5
Number of pages5
JournalInternational journal of Research in Computer Engineering and Electronics
Volume3
Issue number3
Publication statusPublished - 2014
Externally publishedYes

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