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Theoretical results in genetic programming: the next ten years?

, , , and . Genet. Program. Evolvable Mach., 11 (3-4): 285-320 (2010)

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A Distance Between Populations for n-Points Crossover in Genetic Algorithms., , , and . CoRR, (2017)Geometric Semantic Genetic Programming for Real Life Applications., , , and . GPTP, page 191-209. Springer, (2013)Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms., and . GECCO, page 1335-1342. ACM, (2007)Evolutionary Computation: A Brief Overview., and . Genetic and Evolutionary Computation: Medical Applications, Wiley, (2011)A study of search algorithms' optimization speed., , and . J. Comb. Optim., 27 (2): 256-270 (2014)Multi-optimization improves genetic programming generalization ability., , and . GECCO, page 1759. ACM, (2007)The effect of selection from old populations in genetic algorithms., , and . GECCO (Companion), page 161-162. ACM, (2011)Stock index return forecasting: semantics-based genetic programming with local search optimiser., , , and . Int. J. Bio Inspired Comput., 10 (3): 159-171 (2017)Introduction: special issue on parallel and distributed evolutionary algorithms, part I., and . Genet. Program. Evolvable Mach., 10 (4): 339-341 (2009)A Study of Some Implications of the No Free Lunch Theorem., and . EvoWorkshops, volume 4974 of Lecture Notes in Computer Science, page 633-642. Springer, (2008)