We propose a method of knowledge reuse between evolutionary processes that solve different optimization tasks. We define the method in the framework of tree-based genetic programming (GP) and implement it as code reuse between GP trees that evolve in parallel in separate populations delegated to particular tasks. The technical means of code reuse is a crossbreeding operator which works very similar to standard tree-swapping crossover. We consider two variants of this operator, which differ in the way they handle the incompatibility of terminals between the considered problems. In the experimental part we demonstrate that such code reuse is usually beneficial and leads to success rate improvements when solving the common boolean benchmarks.
@INPROCEEDINGS { jaskowski08reuse,
AUTHOR = { Wojciech Ja\'skowski and Krzysztof Krawiec and Bartosz Wieloch },
TITLE = { Multi-Task Code Reuse in Genetic Programming },
BOOKTITLE = { GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation },
YEAR = { 2008 },
PUBLISHER = { ACM },
NOTE = { (to appear) },
ABSTRACT = { We propose a method of knowledge reuse between evolutionary processes that solve different optimization tasks. We define the method in the framework of tree-based genetic programming (GP) and implement it as code reuse between GP trees that evolve in parallel in separate populations delegated to particular tasks. The technical means of code reuse is a crossbreeding operator which works very similar to standard tree-swapping crossover. We consider two variants of this operator, which differ in the way they handle the incompatibility of terminals between the considered problems. In the experimental part we demonstrate that such code reuse is usually beneficial and leads to success rate improvements when solving the common boolean benchmarks. },
COMMENT = { ProjectELP },
}