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📦 AISP Releases

🗂️ Versions pulled from the Releases page

v0.4.1 (2025/10/14)

Changes:

  • Bug fix: Fixed a bug affecting probabilistic mutation logic. Mutated clones were not properly updating the clone_set (initialized with np.empty), which generated a set of invalid mutated clones in clone_and_mutate_continuous (line 61) and clone_and_mutate_ranged (line 158). Issue reported by @gianmarco-lorenti.

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v0.4.0 (2025/09/23)

Changes:

  • New Class: Clonalg - Implementation of the Clonal Selection Algorithm for optimization.

    • Incorporates base classes for optimization algorithms.
    • Supports both maximization and minimization tasks.
    • Mutation functions updated: extended to accept mutation rates.

  • Examples:

    • Added usage examples of Clonalg applied to the Knapsack problem, Traveling Salesman problem and Rastrigin function.

  • Documentation improvements:

    • Markdown documentation for the new classes and methods updated.
    • Fixes to docstrings, better explanations in module docs.

References:

  • Brabazon, Anthony; O’Neill, Michael; McGarraghy, Seán. Natural Computing Algorithms. Springer Berlin Heidelberg, 2015. DOI: 10.1007/978-3-662-43631-8
  • Brownlee, Jason. Clonal Selection Algorithm. Clever Algorithms, 2011. Link

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v0.3.21 (2025/09/21)

Changes:

  • Bug fix: Fix incorrect mutations in clone_and_mutate_ranged function

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v0.3.2 (2025/08/30)

Changes:

  • Updates documentation links in the project’s Markdown files to point to the new references.
  • Optimizes imports across the package to improve code organization and readability.

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v0.3.1 (2025/08/30)

Changes:

  • Fix the KNN implementation in the RNSA algorithm and improve the package documentation.

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v0.3.0 (2025/08/20)

Changes:

  • New Module: INA (Immune Network Algorithm)
  • New Class: AINET, an implementation of the Artificial Immune Network algorithm for data compression and clustering.
    • Data compression inspired by biological immune system principles.
    • Data clustering using Minimum Spanning Tree (MST) based on distances, leveraging scipy.sparse.csgraph.minimum_spanning_tree.

References:

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v0.2.1 (2025/07/27)

Changes:

  • Range Clone Generation: Implementation of functions to clone with range, enabling safe and independent operations with data intervals and ranges.
  • Removal of Required Types: Refactoring to eliminate redundant required types, making the code more flexible and adaptable to different usage scenarios.
  • Type Refactoring: Reorganization and simplification of types to improve clarity, reusability, and code maintainability.

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v0.2.0 (2025/05/25)

Changes:

  • New Algorithm: Implementation of the AIRS algorithm, as detailed in the book Natural Computing Algorithms by Brabazon, O’Neill, and McGarraghy (Springer, 2015).

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v0.1.42 (2025/05/24)

Changes:

  • Standardizes class and method docstrings according to the numpydoc format.
  • Adds automated checks to the CI pipeline to ensure ongoing compliance with the docstring standard.

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v0.1.41 (2025/05/24)

Changes:

  • fixes error in examples caused by changing detectors to private field

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v0.1.40 (2025/05/03)

Changes:

This update aims to improve the package's performance by utilizing Numba for JIT compilation.

  • Python version compatibility:

    • Extended support for Python versions 3.10, 3.11, 3.12, and 3.13.

  • New package added: numba:

    • Added the dependency numba >= 0.59.0.

  • Changes to the negative selection algorithm (NSA):

    • Renamed the base class to BaseNSA and introduced new utilities for distance calculation.
    • Removed methods such as _distance, replacing them with utility functions (e.g., compute_metric_distance).
    • Refactored the management of detectors (_detectors).
    • Modified the logic of methods such as fit, predict, and detector validation, utilizing the Numba decorator in critical sections.

  • New module:

  • Added a new base module for methods shared between different classes, such as the BaseClassifier class for classification methods.

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v0.1.34 (2025/04/06)

Changes:

  • Removed redundant score methods from subclasses of Base. Renamed _score to score in Base, making it public and centralizing the logic.

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v0.1.33 (2024/10/14)

Changes:

  • The package was refactored with the addition of the utils module, which centralizes common functions for reuse in future modules.
  • Unit tests were added for the utils module.
  • In-code comments were updated to English.

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AISP - 0.1.32 (2024/05/05)

Changes:

  • The generation and comparison of detectors using the Hamming distance in the BNSA class has been replaced by the cdist function from scipy to improve generation and prediction performance.

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AISP - 0.1.31 (2024/04/29)

Changes:

Addition:

  • A new method for selecting labels for samples classified as non-self by all detectors.
  • The examples have been adjusted to demonstrate this addition.

Refactoring:

  • The source code has been reviewed and adjusted according to the official Python PEP8 guide.
  • Repositioning of files for better readability and flexibility.

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AISP - 0.1.30 (2023/07/01)

Changes:

Bug fix: The class assignment method for samples that trigger all non-self detectors and become non-self for all classes has been fixed. Now, the method uses the class whose detectors have the greatest average distance from the sample.

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AISP - 0.1.21 (2023/06/15)

Changes:

  • Highlighting the addition of an optional variable in the constructor that allows changing the default value of p==2 in the Minkowski distance.

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AISP - 0.1.1 (2023/06/10)

Changes:

  • Adds the negative selection module to the package.

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