Finding Social Landscapes for PSOs via Kernels

Particle swarm optimiser and genetic algorithm populations are macro-organisms, which perceive their environment as if filtered via a kernel. The kernel assimilates each individual´s sensory abilities so that the collective moves using a greedy hill-climbing strategy. This model is fitted to data collected in real PSO and GA runs by using genetic programming to evolve the kernel. In nature animals tend to live within groups. The social interactions effectively transform the fitness selection landscape seen by an isolated individual. In some cases a group behaves (or even can be said to think) like a single organism. Kernels provide a lens which coarse-grains or averages individual senses and so may help explain joint actions and social responses. The original multi-modal problem is smoothed by convolving it with a problem specific filter designed by GP. Because populations see the transformed social fitness landscape, they can pass over local optima. GP can give a good fit between the predicted behaviour of the macroscopic organism and the actual runs.