{"ID":2847308,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00634","arxiv_id":"2511.00634","title":"Node Preservation and its Effect on Crossover in Cartesian Genetic Programming","abstract":"While crossover is a critical and often indispensable component in other forms of Genetic Programming, such as Linear- and Tree-based, it has consistently been claimed that it deteriorates search performance in CGP. As a result, a mutation-alone $(1+λ)$ evolutionary strategy has become the canonical approach for CGP. Although several operators have been developed that demonstrate an increased performance over the canonical method, a general solution to the problem is still lacking. In this paper, we compare basic crossover methods, namely one-point and uniform, to variants in which nodes are ``preserved,'' including the subgraph crossover developed by Roman Kalkreuth, the difference being that when ``node preservation'' is active, crossover is not allowed to break apart instructions. We also compare a node mutation operator to the traditional point mutation; the former simply replaces an entire node with a new one. We find that node preservation in both mutation and crossover improves search using symbolic regression benchmark problems, moving the field towards a general solution to CGP crossover.","short_abstract":"While crossover is a critical and often indispensable component in other forms of Genetic Programming, such as Linear- and Tree-based, it has consistently been claimed that it deteriorates search performance in CGP. As a result, a mutation-alone $(1+λ)$ evolutionary strategy has become the canonical approach for CGP. A...","url_abs":"https://arxiv.org/abs/2511.00634","url_pdf":"https://arxiv.org/pdf/2511.00634v2","authors":"[\"Mark Kocherovsky\",\"Illya Bakurov\",\"Wolfgang Banzhaf\"]","published":"2025-11-01T17:26:56Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}