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Multi-population genetic programming with adaptively weighted building blocks for symbolic regression

Genetic programming(GP) is a powerful tool to solve Symbolic Regression that requires finding mathematic formula to fit the given observed data. However, existing GPs construct solutions based on building blocks (i.e., the terminal and function set) defined by users in an ad-hoc manner. The search efficacy of GP could be degraded significantly when the size of the building blocks increases. To solve the above problem, this paper proposes a multi-population GP framework with adaptively weighted building blocks. The key idea is to divide the whole population into multiple sub-populations with building blocks with different weights. During the evolution, the weights of building blocks in the sub-populations are adaptively adjusted so that important building blocks can have larger weights and higher selection probabilities to construct solutions. The proposed framework is tested on a set of benchmark problems, and the experimental results have demonstrated the efficacy of the proposed method.