University of Vermont

Department of Computer Science

Research
Evolutionary and Agent-Based Computing

Computer Science faculty members and their graduate students are actively involved in a variety of research projects focusing on systems of autonomous and/or evolving agents.

Some of this research focuses on developing and studying computational intelligence using nature-inspired computing paradigms, such as evolutionary computation, artificial neural networks, and autonomously interacting computational agents. Other projects are focused on applying these methods to domain-specific problems in areas such as robotics, biology, ecology, economics, social networks, psychology, and transportation.

The general area of Evolutionary and Agent-Based Computing has strong overlaps with our other departmental focus areas in Data Mining and Distributed Systems, and supports the College thrust in Complex Systems.

Some examples of current or recent research projects include:

  • Automated design of robot morphology and control, using evolutionary computation and artificial neural networks (Bongard)
  • Active learning in social robots (Bongard)
  • Using genetic programming to evolve models of cyanobacterial population growth (Bongard & Eppstein, in collaboration with Natural Resources Faculty Watson)
  • Using multi-objective evolutionary methods for optimizing management strategies for surface water runoff and innovizing design prinicples (Eppstein, in collaboration with Natural Resources Faculty Bowden)
  • Using stochastic cellular automata to model mechanisms of invasiveness in plant species (Eppstein, in collaboration with Plant Biology Faculty Molofsky)
  • Developing computational evolutionary models of self-organizing biological speciation due to multi-scale nonlinear genetic interactions (Eppstein, in collaboration with Biology Faculty Goodnight)
  • Evolving artificial neural networks to model human decision making (Bongard, in collaboration with Math Faculty Dodds & Danforth)
  • Studying evolutionary and system dynamics on complex networks (Eppstein)
  • Developing a multi-scale agent based model, using spiking neural networks for up-scaling, for modeling the alternative transportation energy economic market (Eppstein, in collaboration with Engineering Faculty Rizzo & Marshall)
  • Design of semantic webs for ecological data (Krivov)
  • Using population based search methods to detect epistatic genetic interactions that predispose for complex disease traits (Eppstein, in collaboration with computational geneticist and adjunct Faculty Moore)
  • Distributed control algorithms for autonomous sensor networks (Wang & Lee, in collaboration with Engineering Faculty Frolik)
  • Pattern recognition in medical imaging (Snapp)
  • Modeling genetic regulatory networks (Bongard)