Genetically Engineered Battle Boids

Antagonistic Flocks Simulation

introduction:
The following Java applets are standard flocking simulations with the addition of offensive and defensive behavior between flocks. Read below for more details or skip directly to the three implementations:

background:
The term 'boids' was first coined by Craig Reynolds in 1986 as the name for his computer simulation of 'coordinated animal motion', or flocking. Since then, many people have dabbled in the subject. Whether toted as a study of bees, flies, frogs, boids, birds, fish, ants, or any other flocking animal, the underlying idea behind flocking algorithms is that complex behavior can emerge from a seemingly innocuous set of rules obeyed locally by each individual in the flock. In most cases, these rules can be summarized as:

Alignment of direction of motion with that of neighbors.
Matching of speed with that of neighbors.
Spatial cohesion with neighbors.
Collision avoidance.

Aside from being interesting in and of itself, the study of flocking lends itself immediately to the study of animal behavior, distributed computing, and collision avoidance. In the most fundamental sense, a flocking simulation is an example of a chaotic system; it's a completely deterministic system that is very sensitive to initial conditions.

welcome to the battlefield:
In addition to implementing all of the above rules characteristic of flocking simulations, these genetically engineered battle boids fight and kill each other, as anything that is properly genetically engineered should. This interaction between different groups of boids brings out a whole host of predatory and defensive behaviors not found in a single flock. Aside from those rules already listed, enabling interaction between different groups entailed implementing the following rules:

Differentiation between 'friends' and 'enemies'.
A sense of when to attack an enemy.
A sense of when to retreat from an enemy.
The notion of death.