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Automatic Search-and-Replace From Examples With Coevolutionary Genetic Programming.

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    This study introduces an automated system for text processing tasks using example-based learning. The system generates search patterns and replacement expressions for efficient text manipulation.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Software Engineering

    Background:

    • Manual text processing and search-and-replace operations can be time-consuming and require technical expertise.
    • Existing methods for automated text manipulation often necessitate complex rule definition or programming knowledge.

    Purpose of the Study:

    • To develop an automated system that generates search-and-replace solutions from simple examples.
    • To enable users without specialized technical skills to perform complex text transformations.

    Main Methods:

    • The system employs genetic programming with cooperative coevolution.
    • Two independent populations evolve search patterns (regular expressions) and replacement expressions.

    Main Results:

    • The system successfully generated effective search-and-replace solutions for six realistic tasks.
    • Achieved high-quality results comparable to challenging baseline methods.

    Conclusions:

    • The proposed example-based system offers an accessible and effective approach to automated text processing.
    • This method simplifies the creation of reusable search-and-replace solutions for various software.