Learning classifier system with deep autoencoder
Kazuma Matsumoto, Yusuke Tajima, Rei Saito, Masaya Nakata, Hiroyuki Satō, Tim Kovacs, Keiki Takadama · 2016
This paper proposes a novel Learning Classifier System (LCS) which integrates Deep AutoEncoder named DAE to solve high-dimensional problems. In the proposed LCS, DAE starts to compress (encode) an environmental input as a high-dimensional information to an input of LCS as a low-dimensional information and decompresses (decodes) an output of LCS as a low-dimensional information to a system output as a high-dimensional information. Since the compressed inputs are encoded by real value, this paper employs XCSR (i.e., an LCS with real value coding) and combines XCSR with DAE. In order to investigate the effectiveness of the proposed LCS, XCSR with DAE, this paper conducts the preliminary experiment on the benchmark classification problem, i.e., 6-Multiplexer problem. The intensive experiments on the compression from 6 to 5 bits have revealed the following implications: (1) XCSR with DAE performs as well as XCSR even learning from the compressed input data; and (2) XCSR with DAE successfully decodes the compressed rules to extract the rules which are the same as those learned with not compressed input data.