Pyramid search: finding solutions for deceptive problems quickly in genetic programming

In deceptive problems many runs lead to suboptimal solutions and it can be difficult to escape from these local optima and find the global best solution. We propose a pyramid search strategy for these kinds of problems. In the pyramid strategy a number of populations are initialised and independently evolved for a number of generations at which point the worst performing populations are discarded. This evolve/discard process is continued until the problem is solved or one population remains. We show that for a number of deceptive problems the pyramid strategy results in a higher probability of success with fewer evaluations and a lower standard deviation of the number evaluations to success than the conventional approach of running to a maximum number of generations and then restarting.