Random search versus genetic programming as engines for collective adaptation

Abstract

We have integrated the distributed search of genetic programming (GP) based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. Since the pure GP approach does not scale well with problem complexity, a natural question is which of the two components is actually contributing to the search process. We investigate a collective memory search that utilizes a random search engine and find that it significantly outperforms the GP-based search engine. We examine the solution space and show that as problem complexity and search space grow, a collective adaptive system will perform better than a collective memory search employing random search as an engine.