A Human Community-Based Genetic Algorithm Model (HCBGA)

• Main Author: Al-Madi, Nagham Azmi Qasim
• Format: Thesis
• Language: English
• Published: 2009
• Subjects: QA75.5-76.95 Electronic computers. Computer science
• Online Access: http://eprints.usm.my/41558/1/Nagham_Azmi_Qasim_Al-Madi24.pdf; http://eprints.usm.my/41558/
• Institution: Universiti Sains Malaysia

Sebagai satu model gelintaran, Algoritma Genetik (GA), telah membuktikan kejayaannya dalam banyak apikasi. Walau bagaimanapun, beberapa penyelidik menyatakan bahawa GA mempunyai “convergence” yang perlahan. Keperlahanan ini berpunca daripada kerawakan dalam kebanyakan operasinya. Oleh itu, ramai penyelidik terkini telah menggunakan populasi berstruktur dalam GA untuk mengurangkan kerawakan seperti model algoritma genetik pulau (IGA), model algoritma genetik bersel (CGA) dan model lain. As a general search model, Genetic Algorithm (GA) has proved its success in many applications. However, several researchers argue that GA has slow convergence. This shortfall is due to the randomness in most of its operations. Hence, recently researches have employed structured populations in GA to reduce this randomness, such as in the island genetic algorithm model (IGA), cellular genetic algorithm model (CGA) and other models.