In evolutionary computation approaches such as genetic programming (GP), preventing
premature convergence to local minima is known to improve performance. As with other
evolutionary computation methods, it can be difficult to construct an effective search bias
in GP that avoids local minima. In particular, it is difficult to determine which features are
the most suitable for the search bias, because GP solutions are expressed in terms of trees
and have multiple features. A common approach intended to local minima is known as
the Island Model. This model generates multiple populations to encourage a global search
and enhance genetic diversity. To improve the Island Model in the framework of GP, we
propose a novel technique using a migration strategy based on textit frequent trees and a
local search, where the frequent trees refer to subtrees that appear multiple times among
the individuals in the island. The proposed method evaluates each island by measuring its
activation level in terms of the fitness value and how many types of frequent trees have
been created. Several individuals are then migrated from an island with a high activation level to an island with a low activation level, and vice versa. The proposed method
also combines strong partial solutions given by a local search. Using six kinds of benchmark problems widely adopted in the literature, we demonstrate that the incorporation of
frequent tree information into a migration strategy and local search effectively improves
performance. The proposed method is shown to significantly outperform both a typical
Island Model GP and the aged layered population structure method.
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