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 which uses 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.
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