Browsing by Author "Bader-el-den, Mohamed"

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Application of supervised machine learning methods on the multidimensional knapsack problem
(Springer, 2021) Rezoug, Abdellah; Bader-el-den, Mohamed; Boughaci, Dalila
Machine Learning (ML) has gained much importance in recent years as many of its effective applications are involved in different fields, healthcare, banking, trading, gaming, etc. Similarly, Combinatorial Optimisation (CO) keeps challenging researchers by new problems with more complex constraints. Merging both fields opens new horizons for development in many areas. This study investigates how effective is to solve CO problems by ML methods. The work considers the Multidimensional Knapsack Problem (MKP) as a study case, which is an np-hard CO problem well-known for its multiple applications. The proposed approach suggests to use solutions of small-size MKP to build models with different ML methods; then, to apply the obtained models on large-size MKP to predict their solutions. The features consist of scores calculated based on information about items while the labels consist of decision variables of optimal solutions calculated from applying CPLEX Solver on small-size MKP. Supervised ML methods build models that help to predict structures of large-size MKP solutions and build them accordingly. A comparison of five ML methods is conducted on standard data set. The experiments showed that the tested methods were able to reach encouraging results. In addition, the study proposes a Genetic Algorithm (GA) that exploits ML outputs essentially in initialisation operator and to repair unfeasible solutions. The algorithm denoted GaPR explores the ML solution neighbourhood as a way of intensification to approach optimal solutions. The carried out experiments indicated that the approach was effective and competitive
MOGA: Multi-Objective Genetic Algorithm to select Stacking Ensemble Learning for classification
(Elsevier, 2025) Rezoug, Abdellah; Bader-el-den, Mohamed
Stacking Ensemble Learning (SEL) has been effectively integrated with Multi-Objective Optimisation (MOO) heuristics for classification tasks across various domains, including finance, healthcare, and cybersecurity. This study aims to address the challenge of generalising SEL to a diverse set of classification cases. Thus, the Multi-Objective Genetic Algorithm (MOGA) framework is proposed, utilising a Genetic Algorithm (GA) to evolve a population of distinct SELs, each built from a varied subset of base models. The goal is to select the subset that composes the most effective SEL for a given classification task. MOGA is designed with two main objectives—maximising precision and recall—which helps to maintain independence from any specific classification case. In addition, incorporating models of varied types ensures adaptability and high performance in different situations. Comprehensive experimentation was conducted on 23 diverse datasets, where MOGA demonstrated high performance in nearly all datasets, outperforming other ensemble learning (EL) methods in 100% of the datasets in precision, 78% in recall, 69.5% in f1−score, and 78% in accuracy. A t-test analysis yielded results of p-value <0.05, indicating a statistically significant improvement in the accuracy of the MOGA over the base models. Moreover, the framework's application can be extended to regression tasks as well