Multi-objective Binary Clonal Selection Algorithm In The Retrieval Phase Of Discrete Hopfield Neural Network With Weighted Systematic Satisfiability - Romli, Nurul Atiqah (2024)
The stability of the Discrete Hopfield Neural Network is dependent on the ability of the network to govern the neuron connections that caused several issues to arise, such as random distribution of positive and negative literals and overfitting final neuron states. Therefore, this thesis proposes a new systematic Satisfiability logical rule namely Weighted Systematic 2 Satisfiability that uses a weighted feature to control the distribution of the negative literals. The proposed logic embedded into Discrete Hopfield Neural Network and considered the optimization of multi-objective function in the retrieval phase to locate superior final neuron states. A Binary Clonal Selection Algorithm is being proposed to ensure optimal generation of the superior final neuron states. The proposed algorithm in the retrieval phase showed optimal performance as compared to the baseline algorithms. The newly proposed logical rule and the algorithm will be the components in the logic mining model namely Weighted Systematic 2 Satisfiability Modified Reverse Analysis. The proposed logic mining model is able to retrieve best induced logic that represents the optimal patterns of the dataset. Based on the findings, the proposed logic mining model outperformed other baseline logic mining models for all the performance metrics used in the repository dataset. The proposed logic mining model was tested on a real-life dataset from the Alzheimer’s Disease Neuroimaging Initiative, and it showed superior performance.