Evolutionary Computation Research Group

The Evolutionary Computation Research Group at BI Lab is interested in developing and analyzing evolutionary learning algorithms for real-world problems such as identification of biochemical networks, design of molecular computers, and disease diagnosis. Current research focuses on probabilistic modeling of evolutionary computations, including Bayesian evolutionary algorithms, sequentical Monte Carlo, and estimation of distribution algorithms, and its applications to the biological knowledge discovery.




  • Genetic Programming, a tutorial presented at SEAL-98, Canberra, Australia, November 24, 1998.
  • Evolutionary Data Mining, presented at SIG Data Mining Workshop of the Korea Management Science Society, Seoul, Korea, October 15, 1998.
  • Program Complexity Control in Genetic Programming, a tutorail presented at GP-97, Stanford, CA, USA, July 1997.
  • Theory and Applications of Evolutionary Computation, a tutorial presented at KISS-96 Fall Conference, Yongin, Korea, October 1996.

Invited Talks

Research Projects

  • LEONN (Learning and Evolution Of Neural Nets): Learning and evolution of biologically inspired neural networks and their application to spatio-temporal pattern recognition, November 1998 - July 2001.
  • EOS (Evolution On Silicon): Hardware evolution and its application to evolutionary design of silicon-based complex adaptive systems, March 1998 - February 2000.
  • MACS (Multi-Agent Cooperation Strategies): Artificial life techniques for evolutionary learning of cooperative behaviors of multiple autonomous mobile robots, September 1996 - August 1999.
  • AGIPT (Adaptive GenetIc Programming Tool): A genetic programming environment for the development of flexible information processing systems, March 1996 - February 1998.
  • ECTOP (Evolutionary Computing Theory for OPtimization): Evolutionary computation theory for solving continuous optimization problems, March 1997 - December 1997.
  • GAMAR (Genetic Algorithms for MulticAst Routing): Evolutionary multicast routing on high speed communication networks, December 1995 - December 1997.
  • ALENN (Active Learning Evolutionary Neural Networks): Active genetic search models for efficient learning in neural networks, March 1996 - December 1996.
  • SIFOGA (Statistical Inference as a theoretical Foundation Of Genetic Algorithms): Statistical theory of genetic algorithms and its application, among others, to neural network optimization for solving time-series prediction problems, GMD, April 1993 - March 1996.

Useful links

This page is maintained by Sang-Woo Lee (slee@bi.snu.ac.kr). Last update: February 21, 2007.