Multi-Level Neutrality in Optimization

This paper explores the idea of neutrality in heuristic optimization algorithms. In particular, the effect of having multiple levels of neutrality in representations is explored. Two experiments using a fitness-adaptive walk algorithm are carried out: the first is concerned with function optimization with Random Boolean Networks, the second with a tunable neutral mapping applied to the hierarchical if-and-only-if function. In both of these cases it is shown that a two-level neutral mapping can be found that performs better than both nonneutral mappings and mappings with a single level of neutrality.