- author = "Qi Chen and Bing Xue and Mengjie Zhang",
- title = "Preserving Population Diversity Based on Transformed Semantics in Genetic Programming for Symbolic Regression",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2021",
- volume = "25",
- number = "3",
- pages = "433--447",
- month = jun,
- keywords = "genetic algorithms, genetic programming, Population Diversity, Symbolic Regression",
- ISSN = "1089-778X",
-
DOI = "
doi:10.1109/TEVC.2020.3046569",
- size = "15 pages",
- abstract = "Population diversity plays an important role in avoiding premature convergence in evolutionary techniques including genetic programming. Obtaining an adequate level of diversity during the evolutionary process has became a concern of many previous researches in genetic programming. This work proposes a new novelty metric for entropy based diversity measure for genetic programming. The new novelty metric is based on the transformed semantics of models in genetic programming, where the semantics are the set of outputs of a model on the training data and principal component analysis is used for a transformation of the semantics. Based on the new novelty metric, a new diversity preserving framework, which incorporates a new fitness function and a new selection operator, is proposed to help genetic programming achieve a good balance between the exploration and the exploitation, thus enhancing its learning and generalisation performance. Compared with two stat-of-the-art diversity preserving methods, the new method can generalise better and reduce the overfitting trend more effectively in most cases. Further examinations on the properties of the search process confirm that the new framework notably enhances the evolvability and locality of genetic programming.",
-
notes = "also known as \cite{9302659}
Evolutionary Computation Research Group at the School of Engineering and Computer Science, Victoria University of Wellington, Wellington 6140, New Zealand.",
