Enhancing Classification Through Multi-view Synthesis in Multi-Population Ensemble Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{khorshidi:2024:GECCOcomp,

• author = "Mohammad Sadegh Khorshidi and Navid Yazdanjue and Hassan Gharoun and Danial Yazdani and Mohammad Reza Nikoo and Fang Chen and Amir H. Gandomi",
• title = "Enhancing Classification Through Multi-view Synthesis in {Multi-Population} Ensemble Genetic Programming",
• booktitle = "GECCO Student Workshop",
• year = "2024",
• editor = "Amir H Gandomi",
• pages = "2099--2102",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, pattern recognition and classification, multipopulation models, multi-view ensemble learning",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3664172",
• size = "4 pages",
• abstract = "This study proposes a genetic programming (GP) approach for classification, integrating cooperative co-evolution with multi-view synthesis. Addressing the challenges of high-dimensional data, we enhance GP by distributing features across multiple populations, each evolving concurrently and cooperatively. Akin to multi-view ensemble learning, the segmentation of the feature set improves classifier performance by processing disparate data {"}views{"}. Individuals comprise multiple genes, with a SoftMax function synthesizing gene outputs. An ensemble method combines decisions across individuals from different populations, augmenting classification accuracy and robustness. Instead of exploring the entire search space, this ensemble approach divides the search space to multiple smaller subspaces that are easier to explore and ensures that each population specializes in different aspects of the problem space. Empirical tests on multiple datasets show that the classifier obtained from proposed approach outperforms the one obtained from a single-population GP executed for the entire feature set.",
• notes = "GECCO-2024 Student Workshop A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Mohammad Sadegh Khorshidi Navid Yazdanjue Hassan Gharoun Danial Yazdani Mohammad Reza Nikoo Fang Chen A H Gandomi

Citations