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In recent years, a new mutation operator, Geometric Semantic Mutation with Local Search (GSM-LS), has been proposed to include a local search step in the mutation process. The core idea of GSM-LS is to incorporate a linear regression step during mutation, thereby accelerating convergence toward high-quality solutions. While GSM-LS helps the convergence of the evolutionary search, it is prone to overfitting. Thus, it was suggested to apply GSM-LS only for a limited number of generations and then revert to standard geometric semantic mutation. A more recently defined variant of\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\mathsf {GSGP}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    (called\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\mathsf {GSGP}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    -reg) also includes a local search step, but shares similar strengths and weaknesses with GSM-LS. Here, we investigate several strategies to mitigate overfitting in GSM-LS and\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\mathsf {GSGP}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    -reg, ranging from simple regularized regression techniques to adaptive methods that estimate overfitting risk at each mutation. The latter approaches partition the training set into two subsets: one used to perform the mutation, and the other to evaluate the risk of overfitting based on the mutation\u2019s impact on held-out data. Experimental evaluations across seven real-world regression benchmarks show that, while plain GSGP underperforms on all datasets, methods incorporating local search often achieve significantly better test performance. For example, on the Airfoil dataset, the GSM-LS variant achieves a median RMSE below 10 compared to 30 with standard GSGP. On the LD50 and Bioavailability datasets, the proposed gen and ridge-regularized variants effectively mitigate overfitting, reducing test RMSE by up to 40% relative to baseline GSGP. We conclude that local search, when used with regularization strategies, enhances GSGP\u2019s performance and generalization capability across a diverse range of tasks.\n                  <\/jats:p>","DOI":"10.1007\/s00500-025-11051-7","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T10:28:09Z","timestamp":1768991289000},"page":"1541-1559","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Local search, semantics, and genetic programming: a global analysis"],"prefix":"10.1007","volume":"30","author":[{"given":"Fabio","family":"Anselmi","sequence":"first","affiliation":[]},{"given":"Mauro","family":"Castelli","sequence":"additional","affiliation":[]},{"given":"Alberto","family":"d\u2019Onofrio","sequence":"additional","affiliation":[]},{"given":"Luca","family":"Manzoni","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3089-6517","authenticated-orcid":false,"given":"Luca","family":"Mariot","sequence":"additional","affiliation":[]},{"given":"Martina","family":"Saletta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"issue":"3","key":"11051_CR1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2996355","volume":"49","author":"A Aleti","year":"2016","unstructured":"Aleti A, Moser I (2016) A systematic literature review of adaptive parameter control methods for evolutionary algorithms. 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