Optimization of artificial flockings by means of anisotropy measurements

An effective procedure to determine the optimal parameters appearing in artificial flockings is proposed in terms of optimization problems. We numerically examine genetic algorithms (GAs) to determine the optimal set of such parameters such as the weights for three essential interactions in BOIDS by Reynolds (1987) under `zero-collision' and `no-breaking-up' constraints. As a fitness function (the energy function) to be maximized by the GA, we choose the so-called the $\textyen gamma$-value of anisotropy which can be observed empirically in typical flocks of starling. We confirm that the GA successfully finds the solution having a large $\textyen gamma$-value leading-up to a strong anisotropy. The numerical experience shows that the procedure might enable us to make more realistic and efficient artificial flocking of starling even in our personal computers.