Multi-population Ensemble Genetic Programming via Cooperative Coevolution and Multi-view Learning for Classification
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arXiv
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| Format: | Preprint |
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2025
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| author | Khorshidi, Mohammad Sadegh Yazdanjue, Navid Gharoun, Hassan Nikoo, Mohammad Reza Chen, Fang Gandomi, Amir H. |
| author_facet | Khorshidi, Mohammad Sadegh Yazdanjue, Navid Gharoun, Hassan Nikoo, Mohammad Reza Chen, Fang Gandomi, Amir H. |
| contents | This paper introduces Multi-population Ensemble Genetic Programming (MEGP), a computational intelligence framework that integrates cooperative coevolution and the multiview learning paradigm to address classification challenges in high-dimensional and heterogeneous feature spaces. MEGP decomposes the input space into conditionally independent feature subsets, enabling multiple subpopulations to evolve in parallel while interacting through a dynamic ensemble-based fitness mechanism. Each individual encodes multiple genes whose outputs are aggregated via a differentiable softmax-based weighting layer, enhancing both model interpretability and adaptive decision fusion. A hybrid selection mechanism incorporating both isolated and ensemble-level fitness promotes inter-population cooperation while preserving intra-population diversity. This dual-level evolutionary dynamic facilitates structured search exploration and reduces premature convergence. Experimental evaluations across eight benchmark datasets demonstrate that MEGP consistently outperforms a baseline GP model in terms of convergence behavior and generalization performance. Comprehensive statistical analyses validate significant improvements in Log-Loss, Precision, Recall, F1 score, and AUC. MEGP also exhibits robust diversity retention and accelerated fitness gains throughout evolution, highlighting its effectiveness for scalable, ensemble-driven evolutionary learning. By unifying population-based optimization, multi-view representation learning, and cooperative coevolution, MEGP contributes a structurally adaptive and interpretable framework that advances emerging directions in evolutionary machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19339 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-population Ensemble Genetic Programming via Cooperative Coevolution and Multi-view Learning for Classification Khorshidi, Mohammad Sadegh Yazdanjue, Navid Gharoun, Hassan Nikoo, Mohammad Reza Chen, Fang Gandomi, Amir H. Neural and Evolutionary Computing Artificial Intelligence 68T20 This paper introduces Multi-population Ensemble Genetic Programming (MEGP), a computational intelligence framework that integrates cooperative coevolution and the multiview learning paradigm to address classification challenges in high-dimensional and heterogeneous feature spaces. MEGP decomposes the input space into conditionally independent feature subsets, enabling multiple subpopulations to evolve in parallel while interacting through a dynamic ensemble-based fitness mechanism. Each individual encodes multiple genes whose outputs are aggregated via a differentiable softmax-based weighting layer, enhancing both model interpretability and adaptive decision fusion. A hybrid selection mechanism incorporating both isolated and ensemble-level fitness promotes inter-population cooperation while preserving intra-population diversity. This dual-level evolutionary dynamic facilitates structured search exploration and reduces premature convergence. Experimental evaluations across eight benchmark datasets demonstrate that MEGP consistently outperforms a baseline GP model in terms of convergence behavior and generalization performance. Comprehensive statistical analyses validate significant improvements in Log-Loss, Precision, Recall, F1 score, and AUC. MEGP also exhibits robust diversity retention and accelerated fitness gains throughout evolution, highlighting its effectiveness for scalable, ensemble-driven evolutionary learning. By unifying population-based optimization, multi-view representation learning, and cooperative coevolution, MEGP contributes a structurally adaptive and interpretable framework that advances emerging directions in evolutionary machine learning. |
| title | Multi-population Ensemble Genetic Programming via Cooperative Coevolution and Multi-view Learning for Classification |
| topic | Neural and Evolutionary Computing Artificial Intelligence 68T20 |
| url | https://arxiv.org/abs/2509.19339 |