Research in computational intelligence (CI) has produced a huge collection of algorithms, grouped into the main CI paradigms. Development of a new CI algorithm requires such algorithm to be thoroughly benchmarked against existing algorithms, which requires researchers to implement already published algorithms. This re-implementation of existing algorithms unnecessarily wastes valuable time, and may be the cause of incorrect results due to unexpected bugs in the code. It is also the case that more, new CI algorithms are hybrids of algorithms from different paradigms. This illustrates a demand for a comprehensive library of CI algorithms, to minimize development time and the occurrence of programming errors, and to facilitate combination of components to form hybrid models. This paper presents such a library, called CIlib.
['Gary Pampara', 'Andries Petrus Engelbrecht', 'T. E. Cloete']
CIlib: A collaborative framework for Computational Intelligence algorithms - Part II