Open Issues Need Help
View All on GitHubAI Summary: Implement functionality to allow users to specify custom filenames and output directories for plots generated by the OOPOA CLI, and add batch plotting capability to process and visualize plots from entire folders.
Official Python implementation of the Object-Oriented Programming Optimization Algorithm (OOPOA). A CLI-based metaheuristic optimizer inspired by encapsulation and inheritance. Based on the 2024 paper by Hosny et al.
Official Python implementation of the Object-Oriented Programming Optimization Algorithm (OOPOA). A CLI-based metaheuristic optimizer inspired by encapsulation and inheritance. Based on the 2024 paper by Hosny et al.
Official Python implementation of the Object-Oriented Programming Optimization Algorithm (OOPOA). A CLI-based metaheuristic optimizer inspired by encapsulation and inheritance. Based on the 2024 paper by Hosny et al.
AI Summary: Implement a `--version` or `-v` flag in the OOPOA CLI's `main.py` to display the current version number using Click's `@cli.version_option` decorator or by retrieving the version from setup metadata. The output should be similar to `oopoa, version 1.0.0`.
Official Python implementation of the Object-Oriented Programming Optimization Algorithm (OOPOA). A CLI-based metaheuristic optimizer inspired by encapsulation and inheritance. Based on the 2024 paper by Hosny et al.
AI Summary: Implement a fallback message in the `oopoa plot` command to handle invalid plot styles gracefully. The message should inform the user of the invalid style and indicate a fallback to the default style. Potentially list available styles.
Official Python implementation of the Object-Oriented Programming Optimization Algorithm (OOPOA). A CLI-based metaheuristic optimizer inspired by encapsulation and inheritance. Based on the 2024 paper by Hosny et al.
AI Summary: Write a pytest unit test for the `evaluate_fitness` function in the `core/population.py` file. The test should create a dummy solution vector, pass it to `evaluate_fitness` using the sphere function, and assert that the returned value is a float.
Official Python implementation of the Object-Oriented Programming Optimization Algorithm (OOPOA). A CLI-based metaheuristic optimizer inspired by encapsulation and inheritance. Based on the 2024 paper by Hosny et al.