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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Symbolic regression (SR) is crucial for automatic programming and data mining.
    • Genetic programming (GP) is a common SR technique, with semantic GP (SGP) showing success.
    • Existing SGP methods struggle with bloat and generalization due to tree-based representations.

    Purpose of the Study:

    • To propose a novel semantic linear GP (SLGP) algorithm.
    • To address limitations of existing SGP methods, specifically bloat and generalization.
    • To enhance the efficiency and effectiveness of symbolic regression.

    Main Methods:

    • Developed a new linear chromosome representation for encoding programs and semantic information.
    • Introduced a novel semantic genetic operator: mutate-and-divide propagation.
    • Recursively propagated semantic error within the linear program structure.

    Main Results:

    • The proposed SLGP algorithm demonstrated superior performance compared to state-of-the-art algorithms.
    • Achieved lower training and test errors in solving symbolic regression problems.
    • Resulted in significantly smaller program sizes, mitigating the bloat issue.

    Conclusions:

    • SLGP offers an effective alternative to traditional SGP methods for symbolic regression.
    • The linear representation and novel genetic operator enhance generalization and reduce bloat.
    • This approach advances the field of semantic genetic programming for complex problem-solving.