Semantic Preserving Feature Partitioning (SPFP) is an algorithm developed in Python that splits a dataset into equally informative feature subsets, enabling multi-view learning. This approach preserves feature meaning while allowing diverse models to be trained from different views, improving robustness and generalization in genetic programming.
View on GitHubGenForge is a powerful Python package developed in Python and designed to push the boundaries of Genetic Programming (GP) through innovative techniques, such as multi-population approaches and solution forging. Whether you're an academic, researcher, or industry professional, GenForge provides a robust framework for developing, analyzing, and deploying GP-based solutions.
View on GitHubGNBG is a MATLAB-based tool for creating customizable benchmark problems in continuous numerical optimization. It allows precise control over problem characteristics such as dimensionality, modality, separability, and landscape complexity, enabling researchers to design test suites that closely match real-world optimization challenges.
View on GitHubEDOLAB is a MATLAB-based platform for education and experimentation in dynamic optimization. It enables the design, testing, and comparison of algorithms in changing problem landscapes, supporting research on adaptability and robustness in evolutionary computation.
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