Author Archives: docurbs

Python Code for Generating Multiplexer Data and Datasets

Generate_Multiplexer A short python script with methods to allow the user to easily generate n-mulitplexer problem data.  Users can either generate single instances, datasets with a specified number of random instances, or complete datasets of all unique multiplexer instances (memory … Continue reading

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Python Code for EK_AF_UCS_2.0 Now Available

EK_AF_UCS_2.0 We have organized, annotated, and cleaned up the code for our published Michigan-Style Learning Classifier System implementations.  EK_AF_UCS stands for Expert Knowledge and Attribute Feedback Supervised Classifier System.  The above code was utilized in the following publication: Tan, J., … Continue reading

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Python Code for AF_UCS_2.0 with Multicore Parallelization Now Available

AF_UCS_2.0_Multicore_Parallelization We have organized, annotated, and cleaned up the code for our published Michigan-Style Learning Classifier System implementations.  AF_UCS stands for Attribute Feedback Supervised Classifier System.  The above code was utilized in the following publications: Rudd, J., Moore, JH., Urbanowicz, … Continue reading

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Python Code for EK_AF_UCS_1.0 Now Available

EK_AF_UCS_1.0 We have organized, annotated, and cleaned up the code for our published Michigan-Style Learning Classifier System implementations.  EK_AF_UCS stands for Expert Knowledge and Attribute Feedback Supervised Classifier System.  The above code was utilized in the following publications: Urbanowicz, R., … Continue reading

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Python Code for AF_UCS_1.0 Now Available

AF_UCS_1.0 We have organized, annotated, and cleaned up the code for our published Michigan-Style Learning Classifier System implementations.  AF_UCS stands for Attribute Feedback Supervised Classifier System.  The above code was utilized in the following publications: Urbanowicz, R., Granizo-Mackenzie, A., Moore, … Continue reading

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Deadline approaching: IWLCS @ GECCO (March 28)

Just a quick reminder that the deadline for the IWLCS will be here in two weeks (March 28).  IWLCS is a great place to present your quality projects and ongoing work related to LCS research.  This year it is particularly … Continue reading

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Python LCS Implementations (GALE & GAssist) for SNP Environment

Urbanowicz_GAssist_2010 Urbanowicz_GALE_2010 The above .zip files contain open source python implementations of existing LCS algorithms (GALE & GAssist) written/modified to accommodate SNP (single nucleotide polymorphism) gene association studies. These are the implementations used in the following paper recently accepted at … Continue reading

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Python LCS Implementations (XCS, UCS, MCS) for SNP Environment

Urbanowicz_XCS_2009 Urbanowicz_UCS_2009 Urbanowicz_MCS_2009 The above .zip files contain open source python implementations of existing LCS algorithms (XCS, UCS, MCS) written/modified to accommodate SNP gene association studies. These are the implementations used in the following paper published in the proceeding of GECCO … Continue reading

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An LCS Review for Beginners and Non-Computer Scientists.

I am pleased to share with you that the Journal of Artificial Evolution and Applications has recently published my LCS Review paper entitled, “Learning Classifier Systems: A Complete Introduction, Review, and Roadmap”. I wrote this from the perspective of a … Continue reading

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