Epilepsi merupakan suatu penyakit neurologi yang sangat lazim dan ditakuti
orang ramai. Banyak kajian telah dibuat untuk membangunkan pengelas automatik
yang dapat memberikan ketepatan yang lebih tinggi. Pengelas automatik ini dapat
membantu doktor dalam mengenali pelbagai segmen isyarat electroencephalography
(EEG) yang berbeza. Dalam kerja penyelidikan ini, suatu model rangkaian neural
wavelet (RNW) telah dicadangkan bagi tujuan pengesanan dan ramalan serangan
epilepsi. Arkitektur dan kon�gurasi RNW dapat ditambah baik menggunakan
pendekatan metaheuristik. Khususnya, algoritma carian harmoni (CH) digunakan
dan diterapkan dalam proses pembelajaran RNW. Tesis ini mengandungi tiga
sumbangan utama. Pertama, algoritma CH digunakan dalam proses pemilihan
�tur. Firstly, the HS algorithm is used in the feature selection stage. The HS algorithm,
which is originally used for optimization problems involving real numbers, is modi�ed
and employed in the task of feature selection, which involves binary values.
Epilepsy is a very common and much-feared neurological disorder. Much research
has been done in developing better automated classi�ers with higher accuracy that
can help clinicians identify the di�erent segments of electroencephalography (EEG)
signals. In this research work, an enhanced wavelet neural network (WNN) model
is proposed for the purpose of epileptic seizure detection and prediction. The
architecture and con�guration of WNNs can be further enhanced using metaheuristic
strategies. Speci�cally, the harmony search (HS) algorithm is employed and
incorporated in the learning of WNNs. The contribution of this thesis is threefold. Firstly, the HS algorithm is used in the feature selection stage. The HS algorithm,
which is originally used for optimization problems involving real numbers, is modi�ed
and employed in the task of feature selection, which involves binary values
