Effectiveness of Multi-step Crossover Fusions in genetic programming

Multi-step Crossover Fusion (MSXF) and deterministic MSXF (dMSXF) are promising crossover operators that perform multi-step neighborhood search between parents, and applicable to various problems by introducing a problem-specific neighborhood structure and a distance measure. Under their appropriate definitions, MSXF and dMSXF can successively generate offspring that acquire parents´ good characteristics along the path connecting the parents. In this paper, we introduce MSXF and dMSXF to genetic programming (GP), and apply them to symbolic regression problem. To optimize trees, we define a neighborhood structure and its corresponding distance measure based on the largest common subtree between parents with considering ordered/unordered tree structures. Experiments using symbolic regression problem instances showed the effectiveness of a GP with the proposed MSXF and dMSXF.