Stephen Majercik

Nature Inspired Optimization Techniques

Nature has provided the inspiration for a number of powerful optimization techniques, among them evolutionary algorithms and swarm based algorithms. These local search metaheuristics have been successfully applied to many difficult real-world problems. In fact, evolutionary algorithms have inspired the Humies, awards for human-competitive published results produced by an evolutionary algorithm; over 50 Humies have been awarded during the last ten years. I will describe one type of evolutionary algorithm–genetic algorithms–and two swarm-based algorithms–ant colony optimization and particle swarm optimization. For each algorithm, I’ll describe the inspiration that produced it, how it works, its benefits and drawbacks, and some real-world applications. Finally, I’ll describe some of my research in particle swarm optimization.

---

Stephen Majercik is an Associate Professor in the Computer Science Department at Bowdoin College. His primary research interest is nature inspired computation, in particular swarm intelligence and particle swarm optimization. He is also interested in applications of artificial intelligence in the arts and using technology as an expressive medium. He received his A.B. in Government from Harvard University in 1977 and his Ph.D. in Computer Science from Duke University in 2000.