  Evolving swarm behaviors with artificial agents is computationally expensive
and challenging. Because reward structures are often sparse in swarm problems,
only a few simulations among hundreds evolve successful swarm behaviors.
Additionally, swarm evolutionary algorithms typically rely on ad hoc fitness
structures, and novel fitness functions need to be designed for each swarm
task. This paper evolves swarm behaviors by systematically combining
Postcondition-Precondition-Action (PPA) canonical Behavior Trees (BT) with a
Grammatical Evolution. The PPA structure replaces ad hoc reward structures with
systematic postcondition checks, which allows a common grammar to learn
solutions to different tasks using only environmental cues and BT feedback. The
static performance of learned behaviors is poor because no agent learns all
necessary subtasks, but performance while evolving is excellent because agents
can quickly change behaviors in new contexts. The evolving algorithm succeeded
in 75\% of learning trials for both foraging and nest maintenance tasks, an
eight-fold improvement over prior work.
