We　use　a　chaotic　evolution　algorithm　to　optimize　the　parameter　of　Gaussian　kernel　function　in　the　kernel　methodbased　autoencoder.　Kernel　method-based　autoencoder　is　an　unsupervised　learning　algorithm　with　the　objective　of　learning　a　representation　for　a　set　of　data.　Kernel　methods　play　an　important　role　in　building　a　kernel　method-based　autoencoder.　There　are　some　options　for　selecting　kernel　functions,　such　as　Gaussian　kernel,　polynomial　kernel,　and　Laplacian　kernel,　etc.　In　each　case,　we　are　required　to　identify　the　parameters　satisfying　the　specified　requirements　or　problems.　Unfortunately,　in　some　cases,　because　of　a　large　range　of　parameters,　we　can　not　select　proper　parameters　manually.　Chaotic　evolution　algorithm　is　one　of　the　optimization　algorithms,　intending　to　obtain　optimal　solutions　for　a　problem,　given　its　certain　solution　search　range.　We　take　advantage　of　chaotic　evolution　algorithm　to　tune　parameters　automatically　for　Gaussian　kernel　function　in　this　work.　We　found　that　the　proposed　method　is　an　efficient　and　effective　tool　to　solve　the　selection　issue　of　kernel　method-based　autoencoder.　©　2019　IEEE.