Titlebook: Genetic Programming; 27th European Confer Mario Giacobini,Bing Xue,Luca Manzoni Conference proceedings 2024 The Editor(s) (if applicable) a

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书目名称Genetic Programming影响因子(影响力)




书目名称Genetic Programming影响因子(影响力)学科排名




书目名称Genetic Programming网络公开度




书目名称Genetic Programming网络公开度学科排名




书目名称Genetic Programming被引频次




书目名称Genetic Programming被引频次学科排名




书目名称Genetic Programming年度引用




书目名称Genetic Programming年度引用学科排名




书目名称Genetic Programming读者反馈




书目名称Genetic Programming读者反馈学科排名

GEST

Generational Computation Reduction in Informal Counterexample-Driven Genetic Programmingolving programs. It has also been extended to combine formal constraints and user-provided training data to solve symbolic regression problems. Here we show how the ideas underlying CDGP can also be applied using only user-provided training data, without formal specifications. We demonstrate the app

organic-matrix

Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual compliance of soft materials endows soft robots with complex behavior, but it also makes their design process unintuitive and in need of automated design. Despite the great interest, evolved virtual soft robots lack the complexity, and co-optimization of morphology and control remains a challenging

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起波澜

DALex: Lexicase-Like Selection via Diverse Aggregationing. In its standard form, lexicase selection filters a population or other collection based on randomly ordered training cases that are considered one at a time. This iterated filtering process can be time-consuming, particularly in settings with large numbers of training cases, including many symb

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antidote

SLIM_GSGP: The Non-bloating Geometric Semantic Genetic Programmingervised learning problems. However, a limitation of GSGP is its tendency to generate offspring larger than their parents, resulting in continually growing program sizes. This leads to the creation of models that are often too complex for human comprehension. This paper presents a novel GSGP variant,

FLIC

Improving Generalization of Evolutionary Feature Construction with Minimal Complexity Knee Points inorithm. While it has achieved great success, a challenging problem in feature construction is the issue of overfitting, which has led to the development of many multi-objective methods to control overfitting. However, for multi-objective methods, a key issue is how to select the final model from the

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