Packages in Python for CEC'2019 100 Digit Competition

[Daniel Molina]

2 minute read

I usually design my evolutionary algorithms in Python (initially for prototyping, but I am too lazy for doing later a version in C/C++ or similar). However, unfortunately, the majority of people in my area work in Matlab 😭. Thus, sometimes I have to wrap the source code for the benchmarks in competition to python ☺️.

This is the story of the my new package at PyPi: https://pypi.org/project/cec2019comp100digit/.

This package is for being able to participate in the CEC’2019 100-Digit Challenge Competition, website here: http://www.ntu.edu.sg/home/epnsugan/index%5Ffiles/CEC2019/CEC2019.htm. That website was the source code in C/C++ and Matlab (using mex), but it was missing Python. This package solves it.

As usual, the complete source code is available at Github.

In the package Pypi page there is more documentation, but in the following I briefly describe the API:

The package is very simple to use. There is a package cec2019comp100digit with three functions:

  • init(fun_id, Dim) Init the function for the dimension selected.

  • eval(sol) Eval the solution, when sol is a numpy (or array) of dimension Dim.

  • end() Free resources.

Installation

It as simple as:

pip install cec2019comp100digit

Requirements:

  • Python3.
  • Numpy.
  • Cython.

Process

  • For init the function.

    from cec2019comp100digit import cec2019comp100digit
bench = cec2019comp100digit
bench.init(3, 10) # Init function 3 with dimension 10

  • To create one or several solutions to eval

    It can be used both numpy and/or array (but only numpy has been actually tested).

    import numpy as np
sol = np.random.rand(10)

  • Evaluate the solution

    It is as simple as:

    fit = bench.eval(sol)

  • Finally, for free the resources

    bench.end()

You can also use it for participate in the competition.

I would like to take this opportunity to remind you that I too am organising another competition, you do not any excuse 😄.