Quantifying evolutionary dynamics of swarm chemistry

This paper reports our recent efforts to quantitatively characterize the evolutionary dynamics of self-organizing patterns observed in Swarm Chemistry. Swarm Chemistry (Sayama 2009) is an artificial chemistry framework that can demonstrate self-organization of dynamic patterns of kinetically interacting heterogeneous particles. A swarm population in Swarm Chemistry consists of a number of simple self-propelled particles moving in a twodimensional continuous space. Each particle can perceive average positions and velocities of other particles within its local perception range, and change its velocity in discrete time steps according to kinetic rules similar to those of Reynolds’ Boids (Reynolds 1987). Each particle is assigned with its own kinetic parameter settings (similar to genotype) that specify preferred speed, local perception range, and strength of each kinetic rule. Particles that share the same set of kinetic parameter settings are considered of the same type. Several model extensions introduced in our recent work, including local information transmission among particles and their stochastic differentiation/re-differentiation, have made the model capable of showing morphogenesis and self-repair (Sayama 2010) and autonomous ecological/evolutionary behaviors of selforganized “super-organisms” made of a number of swarming particles (Sayama 2011; see Fig. 1).