Abstract
Algorithms used in networking, operation research
and optimization can be created using bio-inspired
swarm behaviors, but it is difficult to mimic swarm
behaviors that generalize through diverse environments. State-machine-based artificial collective
behaviors evolved by standard Grammatical Evolution (GE) provide promise for general swarm
behaviors but may not scale to large problems.
This paper introduces an algorithm that evolves
problem-specific swarm behaviors by combining
multi-agent grammatical evolution and Behavior
Trees (BTs). We present a BT-based BNF grammar, supported by different fitness function types,
which overcomes some of the limitations in using GEs to evolve swarm behavior. Given humanprovided, problem-specific fitness-functions, the
learned BT programs encode individual agent behaviors that produce desired swarm behaviors. We
empirically verify the algorithm’s effectiveness on
three different problems: single-source foraging,
collective transport, and nest maintenance. Agent
diversity is key for the evolved behaviors to outperform hand-coded solutions in each task