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The proposed approach utilizes the advantages of both PSO and GA to overcome the local minima problem of ANN, which prevents ANN from improving the classification accuracy. The algorithms start with using backpropagation algorithm, then it keeps repeating applying GA followed by PSO until the optimum classification is reached. The proposed approach is domain independent and has been evaluated by applying it using nine datasets with various domains and characteristics. A comparative study has been performed between the authors' proposed approach and other previous approaches, the results show the superiority of our approach.<\/p>","DOI":"10.4018\/jitr.2017070104","type":"journal-article","created":{"date-parts":[[2017,6,5]],"date-time":"2017-06-05T11:24:21Z","timestamp":1496661861000},"page":"48-68","source":"Crossref","is-referenced-by-count":8,"title":["A Hybrid Approach Based on Genetic Algorithm and Particle Swarm Optimization to Improve Neural Network Classification"],"prefix":"10.4018","volume":"10","author":[{"given":"Nabil M.","family":"Hewahi","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Bahrain, Alsakheer, Bahrain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enas","family":"Abu Hamra","sequence":"additional","affiliation":[{"name":"Islamic University of Gaza, Gaza Strip, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"JITR.2017070104-0","doi-asserted-by":"crossref","unstructured":"Al-Shareef, A., & Abbod, M. 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