Artificial Bee Colony Programming Using Semantic Control Crossover
Akira Hara, Jun-ichi Kushida, Ryota Takemoto, Tetsuyuki Takahama · 2018
Artificial Bee Colony Programming (ABCP), which has been inspired by intelligent foraging behavior of honey bees, is a swarm-based automatic programming method. Tree structural programs can be optimized by three kinds of bees such as employed bees, onlooker bees and scout bees. New solutions are generated by information sharing mechanism, which is similar to subtree exchange crossover used in Genetic Programming (GP). However, it is difficult to control global or local search by the operation. To solve the problem, we introduce Semantic Control Crossover (SCC), which we have previously proposed as one of semantics-based crossovers in GP, into ABCP. In this paper, we proposed two kinds of improved ABCPs using SCC. In the Proposed Method 1, employed bees and onlooker bees have different search strategies. Employed bees perform global search and onlookers perform local search. On the other hand, in the Proposed Method 2, the search strategies are switched according to the degree of stagnation. Local search is performed while solutions have been improved successively. In contrast, global search is performed when the solutions have not been improved for a long term. We applied our proposed methods to symbolic regression problems and confirmed that our proposed methods have higher performance than the conventional ABCP.