- author = "Sadat U. Chowdhury",
- title = "A Study of the Impact of Interaction Mechanisms and Population Diversity in Evolutionary Multiagent Systems",
- school = "City University of New York",
- year = "2016",
- address = "USA",
- month = sep,
- keywords = "genetic algorithms, genetic programming, Artificial Intelligence and Robotics, Computer Sciences, machine learning, artificial life, multiagent systems, robotics, evolutionary systems",
-
URL = "
https://academicworks.cuny.edu/gc_etds/1607",
- size = "xxiv + 257 pages",
-
abstract = "In the Evolutionary Computation (EC) research
community, a major concern is maintaining optimal
levels of population diversity. In the Multiagent
Systems (MAS) research community, a major concern is
implementing effective agent coordination through
various interaction mechanisms. These two concerns
coincide when one is faced with Evolutionary Multiagent
Systems (EMAS).
This thesis demonstrates a methodology to study the relationship between interaction mechanisms, population diversity, and performance of an evolving multiagent system in a dynamic, real-time, and asynchronous environment. An open sourced extensible experimentation platform is developed that allows plug-ins for evolutionary models, interaction mechanisms, and genotypical encoding schemes beyond the one used to run experiments. Moreover, the platform is designed to scale arbitrarily large number of parallel experiments in multi-core clustered environments.
The main contribution of this thesis is better understanding of the role played by population diversity and interaction mechanisms in the evolution of multiagent systems. First, it is shown, through carefully planned experiments in three different evolutionary models, that both interaction mechanisms and population diversity have a statistically significant impact on performance in a system of evolutionary agents coordinating to achieve a shared goal of completing problems in sequential task domains. Second, it is experimentally verified that, in the sequential task domain, a larger heterogeneous population of limited-capability agents will evolve to perform better than a smaller homogeneous population of full-capability agents, and performance is influenced by the ways in which the agents interact. Finally, two novel trait-based population diversity levels are described and are shown to be effective in their applicability.",
- notes = "9-2016 Supervisor: Elizabeth Sklar",
