- author = "Jun Ma3 and Fenghui Gao and Shuangrong Liu and Lin Wang",
- title = "Linear-dependent Multi-interpretation Neuro-Encoded Expression Programming",
- booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion",
- year = "2021",
- editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton",
- pages = "257--258",
- address = "internet",
- series = "GECCO '21",
- month = jul # " 10-14",
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, ANN, Recurrent Neural Network, Neuro-Encoded Expression Programming: Poster",
- isbn13 = "978-1-4503-8351-6",
-
DOI = "
doi:10.1145/3449726.3459498",
- size = "2 pages",
- abstract = "Neuro-Encoded Expression Programming (NEEP) implements the continuous coding for the discrete solution through recurrent neural networks (RNNs), and smooths sharpness of the discrete coding.However, the insertion model generating linear coding in NEEP breaks the coherence of linear coding of RNNs, because the resulting symbols tend to be cluttered when RNNs learn the incoherent sequence relationships. Meanwhile, the redundancy phenomenon that different RNNs generate the same code results in that lots of solutions with the same performance exist in the search space, and causes the decrease for search efficiency. To address these problems, the linear-dependent multi-interpretation NEEP (LM-NEEP)is proposed in this research. LM-NEEP tackles the incoherence problem by employing a linear dependence strategy, and the multi-interpretation strategy is adopted to deal with the redundancy problem in search space. The capability of LM-NEEP is estimated on several symbolic regression problems. The experimental results display that the LM-NEEP significantly outperforms NEEP and some classical genetic programming methods.",
-
notes = "University of Jinan, China
GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)",
