Applicability of Interactive Genetic Algorithms to Multi-agent Systems: Experiments on Games Used in Smart Grid Simulations

  • Yomna Hassan

Student thesis: Master's Thesis


A common goal of many organizations over the next decades is to enhance the efficiency of electrical power grids. This entails : (1) modifying the power grid structure to be able to utilize the available resources in the best way possible, and (2) introducing new energy sources that are able to benefit from the surrounding circumstances. The trend toward the use of renewable energy sources requires the development of power systems that are able to accommodate variability and in- termittency in electricity generation. Therefore, these power grids, usually called 'smart grids', must be dynamic enough to adapt smoothly to changes in the envi- ronment and human preferences. In a smart grid, each decision maker can be represented as an intelligent agent that consumes or produces electricity. Each agent interacts with other agents and the surrounding environment. The goal of these agents may vary between main- taining the stability of electricity in the grid from the generation side and increas- ing users' satisfaction with the electricity service from the consumers' side (which is our focus). This is done through the interaction between different agents to schedule and divide the tasks of consumption and generation among each other, depending on the need and the type of each agent. In this thesis, we investigate the use of interactive genetic algorithms to derive intelligent behavior that enables an agent on the consumer's side to consume the proper amount of electricity to satisfy human preferences. This behavior must take into account the existence of other agents within the system, which increases the dynamicity of the system. In order to evaluate the effectiveness of the suggested algorithms within a multi-agent settings, we test our algorithms in repeated ma- trix games when they associate with other copies of themselves, and against other known multi-agent learning algorithms. We run different variations of the genetic algorithm, with and without human input, in order to determine what are the factors that affect the performance of the algorithm within a dynamic multi-agent system. Our results show reasonable potential for using genetic algorithms in such cir-cumstances, particularly when they utilize effective human input.
Date of Award2011
Original languageAmerican English
SupervisorJacob Crandall (Supervisor)


  • Genetic Algorithms
  • Smart Materials

Cite this