Plenary Lecture
Socio-Cultural Evolution via Neighborhood-Restructuring in Intricate Multi-Layered Networks
Assistant Professor Mostafa Z. Ali
Computer Information Systems
Jordan University of Science & Technology
Irbid, Jordan
E-mail: mzali@just.edu.jo
Abstract: Over the last three decades, many algorithms have been introduced for solving optimization problems of various complexities. Due to the variability of the characteristics in different optimization problems, none of these algorithms performs consistently over a range of problems. Very often due to the lack of communication among researchers from Evolutionary Computation (EC) and other domains of science and engineering like computational electromagnetics, power systems, signal processing, computational chemistry, communication engineering and so on, the non-EC researchers try classical techniques on hard optimization problems and continue with poor results, which could have been substantially improved by applying an EC algorithm.
Previous work in the optimization field of practical problems had shown that cultural learning emerged as the result of meta-level swarming of knowledge sources. Cultural Algorithms employ a basic set of knowledge sources, each related to knowledge observed in various animal species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimization problems using an influence function inspired by the social fabric metaphor. This metaphor provides a framework in which the Knowledge Sources can access the social networks to which individuals can belong, where neighborhood topologies are typically random in terms of the spatial positions of connected neighbors.
We propose an approach that explores the use of a modified multi-layered social fabric with dynamic topologies. This approach is used to restructure the living informational skin created out of the engineered emergence of agents illustrating the tension between the individual and the community in a context of interaction between them. As a diversity preserving-measure, the graph of topology of agents in the formed networks is dynamically and periodically changed, during an algorithm run. The algorithm has been tested on a set of hard real-world problems. Our results suggest that under appropriate parameter settings, the use of the modified graphs of neighborhoods with a probabilistic disruptive re-structuring of the topology produces the best results on the considered test functions compared to the best known results of other algorithms from literature.
Brief Biography of the Speaker: Mostafa Z. Ali, received the Bachelor degree in Applied Mathematics at Jordan University of Science &Technology (JUST), Irbid, Jordan, in 2000. He finished his Masters in Computer Science at the University of Michigan-Dearborn, Michigan, USA in 2003. He finished his Ph.D. in computer science/Artificial Intelligence at Wayne State University, Michigan, USA in 2008.
He is an assistant professor at the department of computer information systems at Jordan University of Science & Technology, Irbid, Jordan. He has more than 36 publications in Journals, conference proceedings, and book chapters. His research interests include artificial intelligence, evolutionary computation, Cultural Algorithms, Virtual Reality, data mining, Bioinformatics Databases, Computer Graphics, and image processing.
Dr. Ali is a member of the IEEE, the IEEE computer society, the American Association of Artificial Intelligence (AAAI), and the ACM.