Awni Hammouri
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Awni Hammouri has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
- Afaf Tareef
Mutah University
1 shared publication - Ahmad Basheer Hassanat
Faculty of Information Technology, Mutah University, Karak 61710, Jordan
1 shared publication - Asma Musabah Alkalbani
Department of Information Technology, College of Computing and Information Sciences, University of Technology and Applied Sciences, Ibri, Oman
1 shared publication - Ban Alrahmani1 shared publication
- Eman Abunawas
Computer Science Department, Mutah University, Karak 61711, Jordan
1 shared publication - Esra’A Alkafaween
Faculty of Science and Information Technology, Alzaytoonah University of Jordan, Amman 11733, Jordan
1 shared publication
Latest Publications
- Open Access
- 1 January 2023
- Published by Engineering and Technology Publishing in Journal of Advances in Information Technology
- Vol. 14 (2) , 224-232
Masked Face Detection and Recognition System Based on Deep Learning Algorithms
- 1 January 2023
- Published by Engineering and Technology Publishing in Journal of Advances in Information Technology
- Vol. 14 (2) , 224-232
Open AccessMasked Face Detection and Recognition System Based on Deep Learning Algorithms
- Top Cited PapersOpen Access
- 10 December 2019
- Published by MDPI AG in Information
- Vol. 10 (12) , 390
Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach
Genetic algorithm (GA) is an artificial intelligence search method that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problems. Integration among (GA) parameters is vital for successful (GA) search. Such parameters include mutation and crossover rates in addition to population that are important issues in (GA). However, each operator of GA has a special and different influence. The impact of these factors is influenced by their probabilities; it is difficult to predefine specific ratios for each parameter, particularly, mutation and crossover operators. This paper reviews various methods for choosing mutation and crossover ratios in GAs. Next, we define new deterministic control approaches for crossover and mutation rates, namely Dynamic Decreasing of high mutation ratio/dynamic increasing of low crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/Dynamic Decreasing of High Crossover (ILM/DHC). The dynamic nature of the proposed methods allows the ratios of both crossover and mutation operators to be changed linearly during the search progress, where (DHM/ILC) starts with 100% ratio for mutations, and 0% for crossovers. Both mutation and crossover ratios start to decrease and increase, respectively. By the end of the search process, the ratios will be 0% for mutations and 100% for crossovers. (ILM/DHC) worked the same but the other way around. The proposed approach was compared with two parameters tuning methods (predefined), namely fifty-fifty crossover/mutation ratios, and the most common approach that uses static ratios such as (0.03) mutation rates and (0.9) crossover rates. The experiments were conducted on ten Traveling Salesman Problems (TSP). The experiments showed the effectiveness of the proposed (DHM/ILC) when dealing with small population size, while the proposed (ILM/DHC) was found to be more effective when using large population size. In fact, both proposed dynamic methods outperformed the predefined methods compared in most cases tested.
- 10 December 2019
- Published by MDPI AG in Information
- Vol. 10 (12) , 390
Top Cited PapersOpen AccessChoosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach
Genetic algorithm (GA) is an artificial intelligence search method that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problems. Integration among (GA) parameters is vital for successful (GA) search. Such parameters include mutation and crossover rates in addition to population that are important issues in (GA). However, each operator of GA has a special and different influence. The impact of these factors is influenced by their probabilities; it is difficult to predefine specific ratios for each parameter, particularly, mutation and crossover operators. This paper reviews various methods for choosing mutation and crossover ratios in GAs. Next, we define new deterministic control approaches for crossover and mutation rates, namely Dynamic Decreasing of high mutation ratio/dynamic increasing of low crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/Dynamic Decreasing of High Crossover (ILM/DHC). The dynamic nature of the proposed methods allows the ratios of both crossover and mutation operators to be changed linearly during the search progress, where (DHM/ILC) starts with 100% ratio for mutations, and 0% for crossovers. Both mutation and crossover ratios start to decrease and increase, respectively. By the end of the search process, the ratios will be 0% for mutations and 100% for crossovers. (ILM/DHC) worked the same but the other way around. The proposed approach was compared with two parameters tuning methods (predefined), namely fifty-fifty crossover/mutation ratios, and the most common approach that uses static ratios such as (0.03) mutation rates and (0.9) crossover rates. The experiments were conducted on ten Traveling Salesman Problems (TSP). The experiments showed the effectiveness of the proposed (DHM/ILC) when dealing with small population size, while the proposed (ILM/DHC) was found to be more effective when using large population size. In fact, both proposed dynamic methods outperformed the predefined methods compared in most cases tested.