@article {Annouch2021607,
	title = {An Adaptive Genetic Algorithm for a New Variant of the Gas Cylinders Open Split Delivery and Pickup with Two-dimensional Loading Constraints},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {12},
	number = {1},
	year = {2021},
	note = {cited By 1},
	pages = {607-619},
	abstract = {This paper studies a combination of two well-known problems in distribution logistics, which are the truck loading problem and the vehicle routing problem. In our context, a customer daily demand exceeds the truck capacity. As a result, the demand has to be split into several routes. In addition, it is required to assign customers to depots, which means that each customer is visited just once by any truck in the fleet. Moreover, we take into consideration a customer time windows. The studied problem can be defined as a Multi-depots open split delivery and pickup vehicle routing problem with two-dimensional loading constraints and time windows (2L-MD-OSPDTW). A mathematical formulation of the problem is proposed as a mixed-integer linear programming model. Then, a set of four class instances is used in a way that reflects the real-life case study. Furthermore, a genetic algorithm is proposed to solve a large scale dataset. Finally, preliminary results are reported and show that the MILP performs very well for small test instances while the genetic algorithm can be efficiently used to solve the problem for a widereaching test instances. {\textcopyright} 2021. All rights reserved.},
	keywords = {Adaptive genetic algorithms, Constraint programming, Fleet operations, Genetic algorithms, Integer programming, Large dataset, Loading constraints, Multi depots, Pickups, Routing algorithms, Sales, Split delivery, Split pickups, Test instances, Time windows, Trucks, Two-dimensional, Two-dimensional loading, Vehicle routing, Vehicle Routing Problems},
	doi = {10.14569/IJACSA.2021.0120170},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100379461\&doi=10.14569\%2fIJACSA.2021.0120170\&partnerID=40\&md5=7e2a6546b01de8c70b8cca5545b09a4e},
	author = {Annouch, A. and Bellabdaoui, A.}
}
@conference {Daosabah2021,
	title = {Integrating Context and Intention for Optimal Semantic Web Service Composition Using AI Planning},
	booktitle = {Proceedings - 4th International Conference on Advanced Communication Technologies and Networking, CommNet 2021},
	year = {2021},
	note = {cited By 0},
	abstract = {This paper deals with an approach for the dynamic composition of web service (WS) based on the user{\textquoteright}s intention and context. Our idea to implement this approach is based on the combination of artificial intelligence (AI) planning techniques and the semantic aspect describing the web service that will be composed. To this end, we will transform the web service composition problem into an AI planning problem. First, conversion of web service semantically described in OWL and OWL-S to an alternative problem and domain description specified in the planning domain description language (PDDL) is made. Then, a planner (AI Planner) using the Genetic Algorithm (GA) features will be invoked, by adapting the PDDL planning language actions to the GA rules, in order to generate the most optimal composition plan (sequence of PDDL actions), which solves the conceived service composition problem, having a goal to achieve (the intention) according to the problem domain description (context). {\textcopyright} 2021 IEEE.},
	keywords = {And OWL, Artificial intelligence, Artificial intelligence planning, Birds, Composition problem, Domain description, Genetic algorithms, Ontology{\textquoteright}s, OWL-S, Planning domain description languages, Quality of service, Semantic Web, Semantic web service compositions, Services composition, Web services, Webs services, Websites},
	doi = {10.1109/CommNet52204.2021.9641912},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123984789\&doi=10.1109\%2fCommNet52204.2021.9641912\&partnerID=40\&md5=582b92b4fd74d9de0d27f804898dee05},
	author = {Daosabah, A. and Guermah, H. and Choukri, I. and Nassar, M.}
}
@conference {Boujida2021102,
	title = {Neural networks based software development effort estimation: A systematic mapping study},
	booktitle = {Proceedings of the 16th International Conference on Software Technologies, ICSOFT 2021},
	year = {2021},
	note = {cited By 0},
	pages = {102-110},
	abstract = {Developing an efficient model that accurately predicts the development effort of a software project is an important task in software project management. Artificial neural networks (ANNs) are promising for building predictive models since their ability to learn from previous data, adapt and produce more accurate results. In this paper, we conducted a systematic mapping study of papers dealing with the estimation of software development effort based on artificial neural networks. In total, 80 relevant studies were identified between 1993 and 2020 and classified with respect to five criteria: publication source, research approach, contribution type, techniques used in combination with ANN models and type of the neural network used. The results showed that, most ANN-based software development effort estimation (SDEE) studies applied the history-based evaluation (HE) and solution proposal (SP) approaches. Besides, the feedforward neural network was the most frequently used ANN type among SDEE researchers. To improve the performance of ANN models, most papers employed optimization methods such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) in combination with ANN models. Copyright {\textcopyright} 2021 by SCITEPRESS {\textendash} Science and Technology Publications, Lda. All rights reserved},
	keywords = {Ann models, Feedforward neural networks, Genetic algorithms, Mapping, Optimization method, Particle swarm optimization (PSO), Predictive analytics, Predictive models, Project management, Research approach, Software design, Software development effort, Software project, Software project management, Systematic mapping studies},
	doi = {10.5220/0010603701020110},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111770639\&doi=10.5220\%2f0010603701020110\&partnerID=40\&md5=c4592c4b704daf3823eb5e9e3e1e5693},
	author = {Boujida, F.E. and Amazal, F.A. and Idri, A.}
}
@conference {Gad2020,
	title = {Cython for Speeding-up Genetic Algorithm},
	booktitle = {2020 International Conference on Electrical and Information Technologies, ICEIT 2020},
	year = {2020},
	note = {cited By 0},
	abstract = {This paper proposes a library for implementing the genetic algorithm using Python mainly in NumPy and speeding-up its execution using Cython. The preliminary Python implementation is inspected for possible optimizations. The 4 main changes include statically defining data types for the NumPy arrays, specifying the data type of the array elements in addition to the number of dimensions, using indexing for looping through the arrays, and finally disabling some unnecessary features in Cython. Using Cython, the NumPy array processing is 1250 times faster than CPython. The Cythonized version of the genetic algorithm is 18 times faster than the Python version. {\textcopyright} 2020 IEEE.},
	keywords = {Array elements, Array processing, Data type, Genetic algorithms, High level languages},
	doi = {10.1109/ICEIT48248.2020.9113210},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086899207\&doi=10.1109\%2fICEIT48248.2020.9113210\&partnerID=40\&md5=2f21b7d3239af190e4a515e4e967104b},
	author = {Gad, A.F. and Ezzahra Jarmouni, F.}
}
@conference {Berahhou2020,
	title = {Dynamic vehicle routing problem with simultaneous delivery and pickup: Formulation and resolution},
	booktitle = {Proceedings - 2020 5th International Conference on Logistics Operations Management, GOL 2020},
	year = {2020},
	note = {cited By 3},
	abstract = {This article treat a fundamental optimization problem encountered by most distribution companies. The Dynamic Vehicle Routing Problem with Simultaneous Delivery and Pickup (DVRPSDP) is a special variant of the VRP, in which new clients comes when the working day has already begun, and a number of vehicles have started their tours, in a way that each customer requires simultaneous delivery and pickup. These new customers must be included in the planned tours as early as possible with a minimum total travel distance. A rich literature review of the problem is carried out. A mathematical model has been formulated for the DVRPSDP, and it is tested with Cplex to provide optimal solutions for small instances. A memetic algorithm has been used to resolve the problem, the proposed algorithm is a combination of the genetic algorithm with a local search method. {\textcopyright} 2020 IEEE.},
	keywords = {Distribution companies, Dynamic vehicle routing problems, Genetic algorithms, Literature reviews, Local search method, Memetic algorithms, Number of vehicles, Optimal solutions, Optimization problems, Pickups, Vehicle routing, Vehicles},
	doi = {10.1109/GOL49479.2020.9314759},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100214538\&doi=10.1109\%2fGOL49479.2020.9314759\&partnerID=40\&md5=103d048003f32e61f1cb306492ddc85f},
	author = {Berahhou, A. and Benadada, Y.}
}
@article {Ezzarii2020,
	title = {Epigenetic algorithm-based detection technique for network attacks},
	journal = {IEEE Access},
	volume = {8},
	year = {2020},
	note = {cited By 2},
	abstract = {Nowadays, the cybersecurity issue involves new strategies to protect against advanced threats and unknown attacks. Intrusion detection system (IDS) is considered a robust system dealing with attacks detection, particularly unknown attacks and anomalies. Several IDS-based algorithms have been recently inspected in the literature, among them the well-known strengthen algorithms, i.e. Genetic algorithm (GA). Moreover, Epigenetic-based algorithm (EGA) is known as an improved version of GA ensuring high performance with reduced computational complexity. Its main goal is to converge within a short time towards an optimal solution by acting on genetic operators, namely mutation and crossover. In this article, we propose a new classifier based on EGA for IDS. Especially, based on a database of network traffics, EGA is applied to classify attacks. The results, performed through EGA simulation, show that the performance of the proposed technique outperforms the ones of GA classifier by obtaining a high detection rate up to 98\% and a faster processing time than that of GA and other algorithms that we have compared in this article. {\textcopyright} 2020 BMJ Publishing Group. All rights reserved.},
	keywords = {Classification (of information), Computer crime, Genetic algorithms, Genetic operators, High detection rate, Intrusion detection, Intrusion Detection Systems, Network attack, Network security, Network traffic, Optimal solutions, Processing time, Unknown attacks},
	doi = {10.1109/ACCESS.2020.3035250},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097226994\&doi=10.1109\%2fACCESS.2020.3035250\&partnerID=40\&md5=2b4d9568d49e06ee5d704619bbb792f2},
	author = {Ezzarii, M. and El Ghazi, H. and El Ghazi, H. and El Bouanani, F.}
}
@article {8699110620120101,
	title = {Efficient Dual Domain Decoding of Linear Block Codes Using Genetic Algorithms.},
	journal = {Journal of Electrical \& Computer Engineering},
	year = {2012},
	pages = {1 - 12},
	abstract = {A computationally efficient algorithm for decoding block codes is developed using a genetic algorithm (GA). The proposed algorithm uses the dual code in contrast to the existing genetic decoders in the literature that use the code itself. Hence, this new approach reduces the complexity of decoding the codes of high rates. We simulated our algorithm in various transmission channels. The performance of this algorithm is investigated and compared with competitor decoding algorithms including Maini and Shakeel ones. The results show that the proposed algorithm gives large gains over the Chase-2 decoding algorithm and reach the performance of the OSD-3 for some quadratic residue (QR) codes. Further, we define a new crossover operator that exploits the domain specific information and compare it with uniform and two point crossover. The complexity of this algorithm is also discussed and compared to other algorithms [ABSTRACT FROM AUTHOR]},
	keywords = {Coding theory, Computational complexity, Data transmission systems, Decoders (Electronics) {\textendash} Software, Genetic algorithms, Performance evaluation},
	issn = {20900147},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=86991106\&site=ehost-live},
	author = {Azouaoui, Ahmed and Belkasmi, Mostafa and Farchane, Abderrazak}
}
@article {8699111820120101,
	title = {Reduced Complexity Iterative Decoding of 3D-Product Block Codes Based on Genetic Algorithms.},
	journal = {Journal of Electrical \& Computer Engineering},
	year = {2012},
	pages = {1 - 8},
	abstract = {Two iterative decoding algorithms of 3D-product block codes (3D-PBC) based on genetic algorithms (GAs) are presented. The first algorithm uses the Chase-Pyndiah SISO, and the second one uses the list-based SISO decoding algorithm (LBDA) based on order-i reprocessing. We applied these algorithms over AWGN channel to symmetric 3D-PBC constructed from BCH codes. The simulation results show that the first algorithm outperforms the Chase-Pyndiah one and is only 1.38 dB away from the Shannon capacity limit at BER of 10-5 for BCH (31, 21, 5){\textthreesuperior} and 1.4 dB for BCH (16, 11, 4){\textthreesuperior}. The simulations of the LBDA-based GA on the BCH (16, 11, 4){\textthreesuperior} show that its performances outperform the first algorithm and is about 1.33 dB from the Shannon limit. Furthermore, these algorithms can be applied to any arbitrary 3D binary product block codes, without the need of a hard-in hardout decoder. We show also that the two proposed decoders are less complex than both Chase-Pyndiah algorithm for codes with large corr},
	keywords = {Computational complexity, Computer simulation, Genetic algorithms, Iterative decoding, Performance evaluation, Three-dimensional imaging},
	issn = {20900147},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=86991118\&site=ehost-live},
	author = {Ahmadi, Abdeslam and Bouanani, Faissal El and Ben-Azza, Hussain and Benghabrit, Youssef}
}
