Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification
Secara umumnya, algoritma genetik (GA) konvensional mempunyai beberapa kelemahan seperti penumpuan pramatang, kecenderungan terperangkap pada penyelesaian optima setempat dan ketidakupayaan penalaan di sekitar kawasan berpotensi. Oleh itu, GA ditambahbaik dengan strategi pencarian, penghasilan s...
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T Technology T Technology Ahmad, Fadzil Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification |
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Secara umumnya, algoritma genetik (GA) konvensional mempunyai beberapa
kelemahan seperti penumpuan pramatang, kecenderungan terperangkap pada
penyelesaian optima setempat dan ketidakupayaan penalaan di sekitar kawasan
berpotensi. Oleh itu, GA ditambahbaik dengan strategi pencarian, penghasilan
semula dan elitisma baharu dicadangkan dalam kajian ini. Pernambahbaikan pertama
melibatkan perubahan kepada struktur operasi GA yang mana ia menumpu pencarian
di sekitar kawasan berpotensi tinggi. Kedua, teknik baharu penghasilan semula yang
dinamakan Segmented Multi-Chromosome Crossover (SMCC) telah diperkenalkan.
Teknik tersebut mengelak kemusnahan maklumat hampir optima yang terkandung
dalam segmen genetik dan membolehkan generasi baharu mewarisi maklumat
penting daripada berbilang induk. Ketiganya, tiga jenis variasi elitism dinamakan
sebagai Best Among Normal and Improved Population (BANI), Best Between
Similar Rank (BBSR) dan Equally Contributed (EQ) telah dibangunkan. Ia
melibatkan pertandingan di kalangan individu terbaik daripada populasi normal dan
ditambahbaik untuk kelangsungan pada generasi selepasnya. GA yang ditambahbaik
kemudiannya digunakan untuk mengoptimasi dan merekabentuk rangkaian
perseptron berbilang lapisan (MLP) secara automatik bagi penyelesaian masalah
pengkelasan corak. Bilangan nod terlindung, nilai pemberat sambungan awalan dan
pemilihan ciri MLP yang memainkan peranan penting dalam menentukan prestasi
pengkelasan dipilih untuk dioptimasi secara automatik oleh GA ditambahbaik.
Prestasi GA ditambahbaik telah dinilai menggunakan fungsi ujian penanda aras yangv
rumit serta berbilang mod dan dibandingkan dengan GA piawai. Berdasarkan
kekerapan sesuatu algoritma menghasilkan keputusan terbaik terhadap fungsi ujian
yang berbeza; ianya telah terbukti bahawa prestasi teknik yang dicadangkan
mengatasi GA piawai. BANI, BBSR dan EQ mencatatkan 30, 18 dan 17 kekerapan
keputusan terbaik masing-masing berbanding GA piawai yang hanya mencatatkan 3
keputusan terbaik. Manakala, prestasi pengkelasan GA-MLP yang ditambahbaik
telah dinilai menggunakan set-set data yang berbeza dari segi saiz ciri kemasukan
dan bilangan kelas keluaran. Keputusan menunjukkan keberkesanan algoritma
baharu daripada segi peratusan ujian kejituan. Peratus peningkatan keseluruhan
sebanyak 0.6%, 0.1% dan 0.3% bagi ujian kejituan dicatatkan oleh BANI, BBSR dan
EQ berbanding dengan GA-MLP piawai. ____________________________________________________________
In general, conventional genetic algorithm (GA) has several drawbacks such as
premature convergence, high tendency to get trapped in local optima solution and
incapable of fine tuning around potential region. Thus, new improved GA that
focuses on new search, reproduction and elitism strategy is proposed in this study.
The first improvement involves changes in the operational structure of GA in which
it concentrates the search in highly potential area in the search region. Secondly, a
novel reproduction technique called Segmented Multi-Chromosome Crossover
(SMCC) is introduced. The proposed technique avoids the destruction of nearly
optimal information contained in the gene segment and allows offspring to inherit
highly important information among multiple parents. Thirdly, three new variations
of elitism scheme namely Best Among Normal and Improved Population (BANI),
Best Between Similar Rank (BBSR) and Equally Contributed (EQ) are developed. It
involves competition among best individuals from normal and improved population
to ensure survival in the next generation. The improved GA is then applied for
optimization and automatic design of multilayer perceptron (MLP) neural network
in solving pattern classification problem. Hidden node size, initial weights and
feature selection of the MLP that play significant role in the classification
performance are selected to be automatically optimized by the improved GA. The
performance of improved GA has been evaluated using highly complicated and
multimodal benchmark test functions and compared with the standard GA. Based on
the occurrences of the best result obtained by an algorithm across different test
functions; it is proven that the proposed method outperforms standard GA. BANI,
BBSR and EQ scores 30, 18 and 17 occurrences respectively compared to the
standard GA that only scores 3 occurrences. Meanwhile, the improved GA-MLP
classification performance has been evaluated using datasets that vary in input
features and output sizes. The results demonstrate the effectiveness of the new
algorithms in term of test accuracy percentage. There is an overall improvement of
0.6%, 0.1% and 0.3% in test accuracy of BANI, BBSR and EQ compared to the
standard GA-MLP. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Ahmad, Fadzil |
| author_facet |
Ahmad, Fadzil |
| author_sort |
Ahmad, Fadzil |
| title |
Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification |
| title_short |
Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification |
| title_full |
Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification |
| title_fullStr |
Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification |
| title_full_unstemmed |
Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification |
| title_sort |
improved genetic algorithm multilayer perceptron network for data classification |
| granting_institution |
Universiti Sains Malaysia |
| granting_department |
Pusat Pengajian Kejuruteraan Elektrik & Elektronik |
| publishDate |
2017 |
| url |
http://eprints.usm.my/47400/1/Improved%20Genetic%20Algorithm%20Multilayer%20Perceptron%20Network%20For%20Data%20Classification.pdf |
| _version_ |
1747821769639591936 |
| spelling |
my-usm-ep.474002021-11-17T03:42:16Z Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification 2017-03-01 Ahmad, Fadzil T Technology TK1-9971 Electrical engineering. Electronics. Nuclear engineering Secara umumnya, algoritma genetik (GA) konvensional mempunyai beberapa kelemahan seperti penumpuan pramatang, kecenderungan terperangkap pada penyelesaian optima setempat dan ketidakupayaan penalaan di sekitar kawasan berpotensi. Oleh itu, GA ditambahbaik dengan strategi pencarian, penghasilan semula dan elitisma baharu dicadangkan dalam kajian ini. Pernambahbaikan pertama melibatkan perubahan kepada struktur operasi GA yang mana ia menumpu pencarian di sekitar kawasan berpotensi tinggi. Kedua, teknik baharu penghasilan semula yang dinamakan Segmented Multi-Chromosome Crossover (SMCC) telah diperkenalkan. Teknik tersebut mengelak kemusnahan maklumat hampir optima yang terkandung dalam segmen genetik dan membolehkan generasi baharu mewarisi maklumat penting daripada berbilang induk. Ketiganya, tiga jenis variasi elitism dinamakan sebagai Best Among Normal and Improved Population (BANI), Best Between Similar Rank (BBSR) dan Equally Contributed (EQ) telah dibangunkan. Ia melibatkan pertandingan di kalangan individu terbaik daripada populasi normal dan ditambahbaik untuk kelangsungan pada generasi selepasnya. GA yang ditambahbaik kemudiannya digunakan untuk mengoptimasi dan merekabentuk rangkaian perseptron berbilang lapisan (MLP) secara automatik bagi penyelesaian masalah pengkelasan corak. Bilangan nod terlindung, nilai pemberat sambungan awalan dan pemilihan ciri MLP yang memainkan peranan penting dalam menentukan prestasi pengkelasan dipilih untuk dioptimasi secara automatik oleh GA ditambahbaik. Prestasi GA ditambahbaik telah dinilai menggunakan fungsi ujian penanda aras yangv rumit serta berbilang mod dan dibandingkan dengan GA piawai. Berdasarkan kekerapan sesuatu algoritma menghasilkan keputusan terbaik terhadap fungsi ujian yang berbeza; ianya telah terbukti bahawa prestasi teknik yang dicadangkan mengatasi GA piawai. BANI, BBSR dan EQ mencatatkan 30, 18 dan 17 kekerapan keputusan terbaik masing-masing berbanding GA piawai yang hanya mencatatkan 3 keputusan terbaik. Manakala, prestasi pengkelasan GA-MLP yang ditambahbaik telah dinilai menggunakan set-set data yang berbeza dari segi saiz ciri kemasukan dan bilangan kelas keluaran. Keputusan menunjukkan keberkesanan algoritma baharu daripada segi peratusan ujian kejituan. Peratus peningkatan keseluruhan sebanyak 0.6%, 0.1% dan 0.3% bagi ujian kejituan dicatatkan oleh BANI, BBSR dan EQ berbanding dengan GA-MLP piawai. ____________________________________________________________ In general, conventional genetic algorithm (GA) has several drawbacks such as premature convergence, high tendency to get trapped in local optima solution and incapable of fine tuning around potential region. Thus, new improved GA that focuses on new search, reproduction and elitism strategy is proposed in this study. The first improvement involves changes in the operational structure of GA in which it concentrates the search in highly potential area in the search region. Secondly, a novel reproduction technique called Segmented Multi-Chromosome Crossover (SMCC) is introduced. The proposed technique avoids the destruction of nearly optimal information contained in the gene segment and allows offspring to inherit highly important information among multiple parents. Thirdly, three new variations of elitism scheme namely Best Among Normal and Improved Population (BANI), Best Between Similar Rank (BBSR) and Equally Contributed (EQ) are developed. It involves competition among best individuals from normal and improved population to ensure survival in the next generation. The improved GA is then applied for optimization and automatic design of multilayer perceptron (MLP) neural network in solving pattern classification problem. Hidden node size, initial weights and feature selection of the MLP that play significant role in the classification performance are selected to be automatically optimized by the improved GA. The performance of improved GA has been evaluated using highly complicated and multimodal benchmark test functions and compared with the standard GA. Based on the occurrences of the best result obtained by an algorithm across different test functions; it is proven that the proposed method outperforms standard GA. BANI, BBSR and EQ scores 30, 18 and 17 occurrences respectively compared to the standard GA that only scores 3 occurrences. Meanwhile, the improved GA-MLP classification performance has been evaluated using datasets that vary in input features and output sizes. The results demonstrate the effectiveness of the new algorithms in term of test accuracy percentage. There is an overall improvement of 0.6%, 0.1% and 0.3% in test accuracy of BANI, BBSR and EQ compared to the standard GA-MLP. 2017-03 Thesis http://eprints.usm.my/47400/ http://eprints.usm.my/47400/1/Improved%20Genetic%20Algorithm%20Multilayer%20Perceptron%20Network%20For%20Data%20Classification.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik & Elektronik |