Mohamed Amine El Majdouli

Mohamed Amine El Majdouli
Mohammed V University of Rabat | um5a · Department of Computing Science

Doctor of Engineering

About

8
Publications
684
Reads
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51
Citations
Additional affiliations
March 2015 - June 2015
Mohammed V University of Rabat, Faculty of Science
Position
  • B.Sc Supervisor
Description
  • The task was supervising a group of 6 B.Sc students during their final projects to obtain their B.Sc degree. The project aims to develop an online M.Sc degree examination tool for B.Sc students.
April 2014 - July 2014
Mohammed V University of Rabat, Faculty of Science
Position
  • Lecturer
Description
  • Developing an iOS app from scratch.
March 2014 - June 2014
Mohammed V University of Rabat, Faculty of Science
Position
  • B.Sc Supervisor
Description
  • The task was supervising a group of 9 B.Sc students during their final projects to obtain their B.Sc degree. The project aims to allow M.Sc supervisor to track the M.Sc students absence and grades via a mobile app.
Education
September 2011 - September 2013
Mohammed V University of Rabat
Field of study
  • Informatique Appliquée au Développement Offshore

Publications

Publications (8)
Conference Paper
This paper introduces a novel memetic solving technique for combinatorial optimization problems called "Lightning Inspired Search Algorithm" (LISA). Indeed, LISA uses a constructive mechanism to build promising solutions and efficiently covers the search space. This mechanism is totally inspired by the natural lightning formation and reported model...
Article
Over the recent years, Fireworks Algorithm has recorded an increasing success on solving continuous optimization problems, due to its efficiency, simplicity and more importantly its rapid convergence to good optimums. Thus, the Fireworks Algorithm performance is now widely comparable with the most popular methods in the optimization field such as e...
Article
Full-text available
This paper presents a novel optimization framework based on the Fireworks Algorithm for Big Data Optimization problems. Indeed, the proposed framework is composed of two optimization algorithms. A single objective Fireworks Algorithm and a multi-objective Fireworks Algorithm are proposed for solving the Big Optimization of Signals problem “Big-OPT”...
Conference Paper
This paper presents a novel adaptation of the Fireworks Algorithm for single objective Big Data Optimization problems. In this context, the developed Single Objective Fireworks Algorithm (SOFWA) is proposed for solving the Big Optimization of Signals “Big-OPT” problem belonging to the Big Data Optimization problems class. Indeed, during an Encephal...
Conference Paper
Per-Instance Algorithm Selection and Automatic Algorithm Configuration have recently gained important interests. However, these approaches face many limitations. For instance, the performance of these methods is deeply influenced by factors like the accuracy of the underlying prediction model, features space correlation, incomplete performance spac...
Conference Paper
This paper investigates the optimization of EEG signals cleaning process by elaborating a comparative study of swarm intelligence, evolutionary and memetic computation techniques. In this context, algorithms from each technique have been selected notably Clonal Selection, Particle Swarm Optimization, Firefly Algorithm, Harmony Search and Fireworks...
Conference Paper
This paper proposes a new pivoting rule named Oriented iterative improvement "OI" for local search heuristics extensively used in metaheuristics solving NP-Hard combinatorial optimization problems. Actually, OI consists in dividing the neighborhood of a solution into many subsets and orients the walk in the search space using information gathered f...

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Projects

Projects (4)
Project
Per-Instance Algorithm Selection and Automatic Algorithm Configuration have recently gained important interests due to the large number of the developed optimization algorithms, which have left the decision maker with a large choice of solvers for a given problem class. However, these approaches face many limitations. For instance, performance of a Per-Instance Algorithm Selection method is deeply influenced by factors like the accuracy of the underlying prediction model, feature space correlation or incomplete performance space for new instances. As for Algorithm Configuration, one of its major issues comes straightforward from its original idea. Actually, Algorithm configuration systems like « IRACE » tries to identify for an algorithm, solving different problem instances, the best possible configuration of its parameters. Unfortunately, this configuration could be the best for an overall performance over all instances, but at the same time, it could be the worst for a small subset of the used instances. In this paper, we describe an effort to address such limitations. A cooperative platform, which we call the WHISPER Platform, composed of a self-adaptive online Algorithm Selection system and an offline Automatic Algorithm Configuration system, working together in order to deliver the most accurate performance. Indeed, the WHISPER approach aims also to deliver a generic framework that integrates any Algorithm Selection model a user would like to work with.
Project
A novel nature-inspired memetic algorithm for permutation-based NP-Hard combinatorial optimization.
Project
A comprehensive study of the underlying real-world problem