The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming
Source abstract
Abstract
Among the evolutionary methods, one that is quite prominent is genetic programming. In recent years, a variant called geometric semantic genetic programming (GSGP) was successfully applied to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all its evolutionary history, i.e., all populations from the first one. We exploit this stored information to define a multi-generational selection scheme that is able to use individuals from older populations. We show that a limited ability to use “old” generations is actually useful for the search process, thus showing a zero-cost way of improving the performances of GSGP.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Evolutionary Algorithms and Applications · Metaheuristic Optimization Algorithms Research · Evolution and Genetic Dynamics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
Dipartimento di Matematica e Geoscienze, Università degli Studi di Trieste, H2bis Building, Via Alfonso Valerio 12/1, 34127 Trieste, Italy
Digital Security Group, Radboud University, P.O. Box 9010, 6500 GL Nijmegen, The Netherlands
Funding
FCT (Fundação para a Ciência e a Tecnologia)
References (29)
Show source-provided references
1. Genetic programming as a means for programming computers by natural selection10.1007/BF00175355 ↗
2. Moraglio, A., Krawiec, K., and Johnson, C.G. (2012, January 1–5). Geometric semantic genetic programming. Proceedings of the International Conference on Parallel Problem Solving from Nature, Taormina, Italy.10.1007/978-3-642-32937-1_3 ↗
3. Vanneschi, L., Castelli, M., Manzoni, L., and Silva, S. (2013, January 3–5). A new implementation of geometric semantic GP and its application to problems in pharmacokinetics. Proceedings of the European Conference on Genetic Programming, Vienna, Austria.10.1007/978-3-642-37207-0_18 ↗
4. GSGP-C++ 2.0: A geometric semantic genetic programming framework10.1016/j.softx.2019.100313 ↗
5. Vanneschi, L., Silva, S., Castelli, M., and Manzoni, L. (2014). Geometric semantic genetic programming for real life applications. Genetic Programming Theory and Practice Xi, Springer.10.1007/978-1-4939-0375-7_11 ↗
6. Louis, S., and Li, G. (1997, January 23–29). Augmenting genetic algorithms with memory to solve traveling salesman problems. Proceedings of the Joint Conference on Information Sciences, Nagoya, Japan.
7. Wiering, M. (2004, January 8–9). Memory-based memetic algorithms. Proceedings of the Benelearn’04—Thirteenth Belgian-Dutch Conference on Machine Learning, Brussels, Belgium.
8. Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments10.1162/evco.2008.16.3.385 ↗
9. Novel Associative Memory Retrieving Strategies for Evolutionary Algorithms in Dynamic Environments
10. Krasnogor, N., and Lanzi, P.L. (2011). The effect of selection from old populations in genetic algorithms. Companion Material Proceedings, Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, Dublin, Ireland, 12–16 July 2011, ACM.
11. A Method to Reuse Old Populations in Genetic Algorithms
12. Augusto, D.A., and Barbosa, H.J.C. (2000, January 22–25). Symbolic Regression via Genetic Programming. Proceedings of the 6th Brazilian Symposium on Neural Networks (SBRN 2000), Rio de Janiero, Brazil.
13. Seront, G. (1995, January 10–12). External concepts reuse in genetic programming. Proceedings of the AAAI Symposium on Genetic programming, MIT/AAAI, Cambridge, MA, USA.
14. Jaskowski, W., Krawiec, K., and Wieloch, B. (2007, January 7–11). Knowledge reuse in genetic programming applied to visual learning. Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2007, London, UK.10.1145/1276958.1277318 ↗
15. Pei, W., Xue, B., Shang, L., and Zhang, M. (2019, January 13–17). Reuse of program trees in genetic programming with a new fitness function in high-dimensional unbalanced classification. Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2019), Prague, Czech Republic.10.1145/3319619.3321958 ↗
16. A Divide-and-Conquer Genetic Programming Algorithm With Ensembles for Image Classification10.1109/TEVC.2021.3082112 ↗
17. The influence of population size in geometric semantic GP10.1016/j.swevo.2016.05.004 ↗
18. Conservation machine learning10.1186/s13040-020-00220-z ↗
19. Conservation machine learning: A case study of random forests10.1038/s41598-021-83247-4 ↗
20. Castelli, M., Trujillo, L., Vanneschi, L., Silva, S., Z-Flores, E., and Legrand, P. (2015, January 11–15). Geometric Semantic Genetic Programming with Local Search. Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain.10.1145/2739480.2754795 ↗
21. Extending Local Search in Geometric Semantic Genetic Programming
22. A survey of semantic methods in genetic programming10.1007/s10710-013-9210-0 ↗
23. Moraglio, A., and Poli, R. (2004, January 26–30). Topological interpretation of crossover. Proceedings of the Genetic and Evolutionary Computation Conference, Seattle, WA, USA.10.1007/978-3-540-24854-5_131 ↗
24. McDermott, J., White, D.R., Luke, S., Manzoni, L., Castelli, M., Vanneschi, L., Jaskowski, W., Krawiec, K., Harper, R., and De Jong, K. (2012, January 7–11). Genetic programming needs better benchmarks. Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Philadelphia, PA, USA.10.1145/2330163.2330273 ↗
25. Archetti, F., Lanzeni, S., Messina, E., and Vanneschi, L. (2007, January 11–13). Genetic programming and other machine learning approaches to predict median oral lethal dose (LD 50) and plasma protein binding levels (% PPB) of drugs. Proceedings of the European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Valencia, Spain.10.1007/978-3-540-71783-6_2 ↗
26. Archetti, F., Lanzeni, S., Messina, E., and Vanneschi, L. (2006, January 8–12). Genetic programming for human oral bioavailability of drugs. Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, Seattle, WA, USA.10.1145/1143997.1144042 ↗
27. Brooks, T.F., Pope, D.S., and Marcolini, M.A. (1989). Airfoil Self-Noise and Prediction, NASA. Technical Report.
28. Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators10.1016/j.eswa.2013.06.037 ↗
29. Pietropolli, G., Manzoni, L., Paoletti, A., and Castelli, M. (2022, January 20–22). Combining Geometric Semantic GP with Gradient-Descent Optimization. Proceedings of the European Conference on Genetic Programming (Part of EvoStar), Madrid, Spain.10.1007/978-3-031-02056-8_2 ↗