Finding attack strategies for predator swarms using genetic algorithms

Abstract- Behavior based architectures have many parameters that must be tuned to produce effective and believable agents. We use genetic algorithms to tune simple behavior based controllers for predators and prey. First, the predator tries to maximize area coverage in a large asymmetric arena with a large number of identically tuned peers. Second, the GA tunes the predator against a single prey agent. Then, we tune two predators against a single prey. The prey evolves against a default predator and an evolved predator. The genetic algorithm finds high-performance controller parameters after a short length of time and outpaces the same controllers hand tuned by human programmers after only a small number of evaluations.

• Type: Conference
• Year: 2005
• Where: CEC
• Authors: Ryan E. Leigh, Tony Morelli, Sushil J. Louis, Monica N. Nicolescu, Chris Miles
• Keywords: Artificial Intelligence, CEC 2005, Genetic Algorithm, Predator, Single Prey