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10
Latest Version
Avg Release Cycle
261 days
Latest Release
1527 days ago

Changelog History

  • v2.0.1 Changes

    March 21, 2018

    ๐Ÿš€ This is the first release of CIlib 2.0, a library for computational intelligence. This version focuses on correctness, type-safety and, most importantly, the ability to perfectly reproduce results.

    ๐Ÿ‘ท The project is divided up into several modules, each providing the minimum amount of functionality for a given CI algorithmic metaphor. Examples of modules include modules for PSO, GA, DE and EDA algorithms.

    Given that this is the first release of the library, what follows is a general description of the main features within the library. For usage details and examples, please consult either the project website, the examples directory in the project source, or the community created tutorial book.

    For more information, please consult the scaladoc and come join the community on gitter.im

    Controlled Randomness

    Probably the most important aspect of the library is to control the effects of randomness within an algorithm and problem. Often, these PRNGs are created on the fly in an ad-hoc manner, or they are simply sampled from the provided PRNG of the host language or the operating system. Although these may be "good enough" in most cases, their usage is simply unacceptable in CI. The PRNG used and the manner in which the values from the PRNG are sampled creates a snowball effect within the algorithm as the next value produced from the PRNG is dependant on how the previous value was produced.

    For this reason, the randomness has been extracted into an effect that is tracked and maintained by the core data structures of the library. Because of this tracking behaviour, it is also possible replicate and repeat experiments or simulations to achieve the exact same results. Additionally, this tracking forces the user to provide the source of randomness, but only at the point where an algorithm is to be executed.
    Composition

    The library is built with composition in mind. This allows the same pieces of logic to be reused in a variety of ways, preventing duplication and most importantly, allowing for simpler experimentation. Creating larger pieces of logic from smaller pieces is a very desirable property to have.

    Type-safety

    The library is implemented in a purely functional way, favoring immutability. Using immutable structures and pure functions prevents a whole series of errors, which is just too valuable to ignore.

    ๐Ÿ‘€ Furthermore, where possible, as many errors will be reported to the user during compile time. Although this may seem very inconvenient, the benefits far out-weigh the perceived disadvantages. One of the main ideas with the design and implementation of the library is that if the code compiles, it will execute. This does not mean that there will be no errors - that's a foolish thing to say - but what it does mean is that any problems will be logic errors and not related to the structure of the resulting algorithm and problem definitions.

    Explicit focus on algorithms

    ๐Ÿ“‡ It's very tempting to expand a software project to eventually support everything, but it is not the correct way nor a good idea. To this end, CIlib will provide the user with the tools needed to execute algorithms and perform measurements on the results of the algorithms. These results can then be written to different file formats (CSV or Parquet). Once the results have been obtained, it is recommended that the user then use these data files within existing analysis frameworks. Many such tools already exist (R / Spark / Pandas / etc) and the file formats supported by CIlib can be read by these packages without much effort. It should be noted that parquet is the preferred format, not only because the resulting file is smaller than that of a CSV, but because the format contains metadata about the data columns it maintains, and that this metadata can be used within the analysis tools.

    The format of this data is also defined based on a data structure that the user provides.


    Note about version 1.0.x

    โšก๏ธ The version 1.x series of CIlib was a complete framework, completely based around a simulation program. This series has been deprecated and will no longer be updated, but is kept within the repository purely for prosperity. There are several problems with this implementation and was the inspiration for the current series of CIlib.

  • v2.0.0 Changes

    March 19, 2018

    ๐Ÿš€ This is the first release of CIlib 2.0, a library for computational intelligence. This version focuses on correctness, type-safety and, most importantly, the ability to perfectly reproduce results.

    The project is divided up into several modules, each providing the ๐Ÿ‘ท minimum amount of functionality for a given CI algorithmic metaphor. Examples of modules include modules for PSO, GA, DE and EDA algorithms.

    ๐Ÿš€ Given that this is the first release of the library, what follows is a general description of the main features within the library. For usage details and examples, please consult either the project website, the examples directory in the project source, or the community created tutorial ๐Ÿš€ book

    For more information, please consult the scaladoc and come join the community on gitter.im

    Controlled Randomness

    Probably the most important aspect of the library is to control the effects of randomness within an algorithm and problem. Often, these PRNGs are created on the fly in an ad-hoc manner, or they are simply sampled from the provided PRNG of the host language or the operating system. Although these may be "good enough" in most cases, their usage is simply unacceptable in CI. The PRNG used and the manner in which the values from the PRNG are sampled creates a snowball effect within the algorithm as the next value produced from the PRNG is dependant on how the previous value was produced.

    For this reason, the randomness has been extracted into an effect that is tracked and maintained by the core data structures of the library. Because of this tracking behaviour, it is also possible replicate and repeat experiments or simulations to achieve the exact same results. Additionally, this tracking forces the user to provide the source of randomness, but only at the point where an algorithm is to be executed.

    Composition

    The library is built with composition in mind. This allows the same pieces of logic to be reused in a variety of ways, preventing duplication and most importantly, allowing for simpler experimentation. Creating larger pieces of logic from smaller pieces is a very desirable property to have.

    Type-safety

    The library is implemented in a purely functional way, favoring immutability. Using immutable structures and pure functions prevents a whole series of errors, which is just too valuable to ignore.

    Furthermore, where possible, as many errors will be reported to the ๐Ÿ‘€ user during compile time. Although this may seem very inconvenient, the benefits far out-weigh the perceived disadvantages. One of the main ideas with the design and implementation of the library is that if the code compiles, it will execute. This does not mean that there will be no errors - that's a foolish thing to say - but what it does mean is that any problems will be logic errors and not related to the structure of the resulting algorithm and problem definitions.

    Explicit focus on algorithms

    ๐Ÿ‘ It's very tempting to expand a software project to eventually support everything, but it is not the correct way nor a good idea. To this end, CIlib will provide the user with the tools needed to execute algorithms and perform measurements on the results of the algorithms. These results can then be written to different file formats (CSV or Parquet). Once the results have been obtained, it is recommended that the user then use these data files within existing analysis frameworks. Many such tools already exist (R / Spark / Pandas ๐Ÿ‘ / etc) and the file formats supported by CIlib can be read by these ๐Ÿ“ฆ packages without much effort. It should be noted that parquet is the preferred format, not only because the resulting file is smaller than ๐Ÿ“‡ that of a CSV, but because the format contains metadata about the data ๐Ÿ“‡ columns it maintains, and that this metadata can be used within the analysis tools.

    The format of this data is also defined based on a data structure that the user provides.


    Note: The version 1.x series of CIlib is a complete framework, completely based around a simulation program. This series has been โšก๏ธ deprecated and will no longer be updated, but is kept within the repository purely for prosperity. There are several problems with this implementation and was the inspiration for the current series of CIlib.

  • v2.0.0-RC1 Changes

    February 28, 2018

    โšก๏ธ Updated algorithm execution to be stream based.

  • v2.0.0-M3 Changes

    February 11, 2018

    ๐Ÿš€ This is the third and possibly last milestone release for CIlib v2.0

  • v2.0.0-M2

    February 28, 2018
  • v2.0.0-M1

    February 28, 2018
  • v1.0.x

    February 16, 2016
  • v0.8

    July 26, 2013
  • v0.8.0-RC1

    June 01, 2013
  • v0.7.6

    March 18, 2013