The EVOML Research Group, under the leadership of Professors Ibrahim Aljarah​, and Hossam Faris , is dedicated to advancing the fields of evolutionary algorithms and machine learning. Their research encompasses a wide array of applications, including medicine, business, manufacturing, industry, education, and environmental modeling.

Key Contributions and Projects:

• Harris Hawks Optimization (HHO): In 2019, the group introduced the Harris Hawks Optimization algorithm, inspired by the cooperative hunting strategies of Harris' hawks. This population-based, gradient-free optimization algorithm has been applied to various complex optimization problems.

  
  

• EvoCluster Framework: The team developed EvoCluster, an open-source nature-inspired optimization clustering framework implemented in Python. EvoCluster integrates multiple metaheuristic optimizers, objective functions, and evaluation measures, facilitating efficient data clustering.

  
  

• EvoCC Framework: Building upon EvoCluster, the group created EvoCC, a classification-based nature-inspired optimization clustering framework. EvoCC combines clustering, classification, and evolutionary computation methods to enhance classification processes across various domains.

  
  

• Publication of "Evolutionary Data Clustering: Algorithms and Applications": In 2021, the group published a comprehensive book detailing evolutionary clustering techniques and their applications across diverse fields. This work serves as a valuable resource for researchers and practitioners in the domain.

  
  

Collaborations and Recognitions:

The EVOML Research Group actively collaborates with international scholars, leading to joint publications in esteemed academic journals. Their innovative contributions have been recognized through various honors, including the selection of Professor Aljarah, and Professor Faris as a Highly Cited Researcher by Clarivate in 2021.

Through these initiatives, the EVOML Research Group continues to make significant strides in evolutionary algorithms and machine learning, addressing complex challenges across multiple sectors.​

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