Many real-world problems are dynamic in some way and require adaptation over time. While we often solve static versions of these problems, there are often advantages to dynamic optimization. Recently, a great deal of attention has been paid to improving the performance of dynamic optimization, particularly dynamic optimization with evolutionary algorithms. Very often, the dynamic nature of problems has some structure, which can often be exploited to improve performance. The use of memory in dynamic optimization is one way to exploit structure in dynamic problems. I am currently investigating the use of indirect and model-building memories for dynamic problems.

• Gregory J. Barlow and Stephen F. Smith. "A Memory Enhanced Evolutionary Algorithm for Dynamic Scheduling Problems." Applications of Evolutionary Computing: EvoWorkshops 2008. Naples, Italy. March 2008. EvoSTOC Best Paper Award. (abstract, bib, ps, ps.gz, pdf)

While most work in evolutionary robotics has been on wheeled mobile robots, unmanned aerial vehicles (UAVs) present a real opportunity for evolved control. In my master's thesis, I investigated the evolution of navigation controllers for UAVs. In subsequent work, I have done robustness testing for UAV controllers evolved in simulation in order to select a controller for transference to a real UAV. I have also evolved multi-UAV teams for distributed tasks.

• Gregory J. Barlow and Choong K. Oh. "Evolved Navigation Control for Unmanned Aerial Vehicles." Frontiers in Evolutionary Robotics. Ed. Hitoshi Iba. Vienna: I-Tech Education and Publishing, 2008. 353-378. (bib)
• Gregory J. Barlow, Choong K. Oh, and Stephen F. Smith. "Evolving Cooperative Control on Sparsely Distributed Tasks for UAV Teams Without Global Communication." Proceedings of the 2008 Genetic and Evolutionary Computation Conference. Atlanta, Georgia. July 2008. (abstract, bib)
• Gregory J. Barlow and Choong K. Oh. "Robustness Analysis of Genetic Programming Controllers for Unmanned Aerial Vehicles." Proceedings of the 2006 Genetic and Evolutionary Computation Conference. Seattle, WA. July 2006. (abstract, bib, ps, ps.gz, pdf)
• Gregory J. Barlow, Choong K. Oh, and Edward Grant. "Incremental Evolution of Autonomous Controllers for Unmanned Aerial Vehicles using Multi-objective Genetic Programming." Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems (CIS). Singapore. December 2004. pp. 688-693. (abstract, bib, ps, ps.gz, pdf)
• Gregory J. Barlow. "Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-objective Genetic Programming." Master's thesis. North Carolina State University. Raleigh, NC. March 2004. (abstract, bib, ps, ps.gz, pdf)

While I was at the Center for Robotics and Intelligent Machines at NC State, I also worked on evolutionary robotics for wheeled mobile robots. For this research, we used the EvBot and EvBot II platforms developed in-house. Much of this work was done with Andrew Nelson on his Ph.D research on evolved neural controllers for robot colonies. I also evolved genetic programming controllers for a radar tracking task (originally intended for UAVs) and transferred the controllers to the EvBot II platform. A passive sonar system on the robot was used in place of the radar sensor, and a speaker emitting a tone was used as the target in place of a radar.

• Andrew L. Nelson, Gregory J. Barlow, and Lefteris Doitsidis. "Fitness Functions in Evolutionary Robotics: A Survey and Analysis." Robotics and Autonomous Systems. Accepted, 2008. (bib)
• Gregory J. Barlow, Leonardo S. Mattos, Edward Grant, and Choong K. Oh. "Transference of Evolved Unmanned Aerial Vehicle Controllers to a Wheeled Mobile Robot." Proceedings of the IEEE International Conference on Robotics and Automation. Barcelona, Spain. April 2005. pp. 2087-2092. (abstract, bib, ps, ps.gz, pdf)
• Andrew L. Nelson, Edward Grant, Gregory J. Barlow, and Thomas C. Henderson. "A colony of robots using vision sensing and evolved neural controllers." Proceedings of the IEEE Conference on Intelligent Robots and Systems. Las Vegas, NV. October 2003. pp. 2273-2278. (abstract, bib, pdf)
• Andrew L. Nelson, Edward Grant, Gregory Barlow, and Mark White. "Evolution of Complex Autonomous Robot Behaviors using Competitive Fitness." Proceedings of the IEEE International Conference on Integration of Knowledge Intensive Multi-Agent Systems. Boston, MA. September 2003. pp. 145-150. (abstract, bib, pdf)

The EvBot research platform was developed at the Center for Robotics and Intelligent Machines at NC State. John Galeotti developed both the hardware and software for the first EvBot, and Leonardo Mattos developed the hardware for the EvBot II. While I was an undergraduate, I developed the upgraded software for the EvBot II, developed a communications protocol for the robot colony, and helped to automate many of the robot experiments we were doing at the time. In addition to the evolutionary robotics work we did using the EvBot colony, we also used the colony for experiments on leadership protocols for distributed sensor networks.

• Gregory J. Barlow, Thomas C. Henderson, Andrew L. Nelson, and Edward Grant. "Dynamic Leadership Protocol for S-nets." Proceedings of the IEEE International Conference on Robotics and Automation. New Orleans, LA. April 2004. pp. 1091-1096. (abstract, bib, ps, ps.gz, pdf)
• "Evolutionary Robotics Research Project." Industry/Research News. IEEE Robotics & Automation Magazine. March 2005. pg. 79. (pdf)