Multi-population Ensemble Genetic Programming via Cooperative Coevolution and Multi-view Learning for Classification

• URL: http://arxiv.org/abs/2509.19339v1
• Date: Tue, 16 Sep 2025 01:32:04 GMT
• Title: Multi-population Ensemble Genetic Programming via Cooperative Coevolution and Multi-view Learning for Classification
• Authors: Mohammad Sadegh Khorshidi, Navid Yazdanjue, Hassan Gharoun, Mohammad Reza Nikoo, Fang Chen, Amir H. Gandomi,
• Abstract summary: Multi-population Ensemble Genetic Programming (MEGP) is a computational intelligence framework that integrates cooperative coevolution and the multiview learning paradigm.<n>MEGP decomposes the input space into conditionally independent feature subsets, enabling multiple subpopulations to evolve in parallel.<n>It consistently outperforms a baseline GP model in terms of convergence behavior and generalization performance.
• Score: 9.17282078449475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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