Multiobjective Evolutionary Computation

MCEA/D

• This is an open source code of Multiple Classifiers-assisted Evolutionary Algorithm based on Decomposition (MCEA/D), implemented by MATLAB.

• You can easily use this code on Evolutionary multi-objective optimization platform (PlatEMO) on MATLAB.

• Please refer the following article:

Takumi Sonoda and Masaya Nakata, Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multi-Objective Problems, IEEE Transactions on Evolutionary Computation, Vol.26, No.6, pp.1581--1595, IEEE, Mar, 2022.

Evolutionary Rule-based Learning

XCS Theory-C# for binary coding

• This is an open source code of the XCS classifier system with theoretical parameter settings for binary coding, implemented by C#.

• You can easily use this code on Microsoft Visual Studio 2019 (free for Community): open ".sln" file and then press key "F5" (i.e. run).

• Note that, for speeding up, this code employs a "messy-coding" like rule-matching process with parallel computing, which returns the same result of the normal matching process.

• Please refer the following article:

Masaya Nakata and Will N. Browne, Learning Optimality Theory for Accuracy-based Learning Classifier System, IEEE Transactions on Evolutionary Computation, Vol.25, No.1, pp.61--74, IEEE, Feb 2021.

XCS Theory-C# for real-value coding

• This is an open source code of the XCS classifier system with theoretical parameter settings for real-value coding (the lower-upper coding), implemented by C#.

• You can easily use this code on Microsoft Visual Studio 2019 (free for Community): open ".sln" file and then press key "F5" (i.e. run).

• Note that, for speeding up, this code employs a "messy-coding" like rule-matching process with parallel computing, which returns the same result of the normal matching process.

• Please refer the following article:

Masaya Nakata and Will N. Browne, Learning Optimality Theory for Accuracy-based Learning Classifier System, IEEE Transactions on Evolutionary Computation, Vol.25, No.1, pp.61--74, IEEE, Feb 2021.