Publications on Evolutionary Intelligence

 

Evolutionary Algorithms

  • Evolutionary concept learning from cartoon videos by multimodal hypernetworks, B. J. Lee, J. W. Ha, K. M. Kim, and B. T. Zhang, IEEE Congress on Evolutionary Computation (CEC 2013), pp. 1186-1192, 2013.[PDF]
  • A probabilistic coevolutionary biclustering algorithm for discovering coherent patterns in gene expression dataset, J.-G. Joung, S.-J. Kim, S.-Y. Shin and B.-T. Zhang, BMC Bioinformatics, 13(Suppl 17):S12, 2012. [PDF]
  • Evolutionary particle filtering for sequential dependency learning from video data, J.H. Yoo, H.-S. Seok, and B.-T. Zhang, IEEE World Congress Computational Intelligence (WCCI-CEC 2012), pp. 559-566, 2012. [PDF]
  • Evolving a population code for multimodal concept learning, B. Lee, H.-S. Seok, and B.-T. Zhang, IEEE Congress on Evolutionary Computation (CEC 2011), pp. 809-816, 2011.[PDF]
  • A molecular evolutionary algorithm for learning hypernetworks on simulated DNA computers, J.-H. Lee, B. Lee, J.S. Kim, R. Deaton, and B.-T. Zhang, IEEE Congress on Evolutionary Computation (CEC 2011), pp. 2845-2852, 2011.[PDF]
  • Evolutionary layered hypernetworks for identifying microRNA-mRNA regulatory modules, S.-J. Kim, J.-W. Ha, B. Lee, and B.-T. Zhang, IEEE World Congress Computational Intelligence (WCCI-CEC 2010), pp. 2299-2306, 2010. [PDF]
  • EvoOligo: Oligonucleotide probe design with multiobjective evolutionary algorithms, S.-Y. Shin, I.-H. Lee, Y.-M. Cho, K.-A. Yang, and B.-T. Zhang, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 39(6):1606-1616, 2009.[PDF]
  • Evolving hypernetwork models of binary time series for forecasting price movements on stock markets, E. Bautu, S. Kim, A. Bautu, H. Luchian, and B.-T. Zhang, IEEE Congress on Evolutionary Computation (CEC 2009), pp.166-173, 2009.[PDF]
  • Evolutionary hypernetwork classifiers for protein-protein interaction sentence filtering, J. Bootkrajang, S. Kim, and B.-T. Zhang, The Genetic and Evolutionary Computation Conference (GECCO 2009), pp. 185-191, 2009.[PDF]
  • Evolutionary hypernetworks for learning to generate music from examples, H.-W. Kim, B.-H. Kim, and B.-T. Zhang, IEEE International Conference on Fuzzy Systems (Fuzz IEEE 2009), pp. 47-52, 2009.[PDF]
  • Evolutionary multiobjective optimization for DNA sequence design, S.-Y. Shin, I.-H. Lee, B.-T. Zhang, Multi-Objective Optimization in Computational Intelligence: Theory and Practice, Chapter 9, Information Science Reference, 2008.
  • Dinucleotide step parameterization of pre-miRNAs using multi-objective evolutionary algorithms, J.-W. Nam, I.-H. Lee, K.-B. Hwang, S.-B. Park, and B.-T. Zhang, Lecture Notes in Computer Science, EvoBio 2007, 4447:176-186, 2007.[PDF]
  • Multiplex PCR assay design by hybrid multiobjective evolutionary algorithm, I.-H. Lee, S.-Y. Shin, B.-T. Zhang, Lecture Notes in Computer Science, EMO 2007, 4403:376-385, 2007. [PDF]

 

Bayesian Evolutionary Algorithms

  • Bayesian evolutionary hypergraph learning for predicting cancer clinical outcomes, S.-J Kim, J.-W. Ha, and B.-T. Zhang, Journal of Biomedical Informatics, 2014 (in press).
  • Evolutionary optimization by distribution estimation with mixtures of factor analyzers, Cho, D.-Y. and Zhang, B.-T., Proceedings of the 2002 Congress on Evolutionary Computation (CEC2002), 2:1396-1401, 2002. [PDF]
  • A Bayesian evolutionary approach to the design and learning of heterogeneous neural trees, Zhang, B.-T., Integrated Computer-Aided Engineering, 9(1):73-86, 2002. [PDF]
  • System identification using evolutionary Markov chain monte carlo, Zhang, B.-T. and Cho, D.-Y., Journal of Systems Architecture, 47(7):587-599, 2001. [PDF]
  • Continuous estimation of distribution algorithms with probabilistic principal component analysis, Cho, D.-Y. and Zhang, B.-T., Proceedings of the 2001 Congress on Evolutionary Computation (CEC2001), 1:521-526, 2001. [PDF]
  • Bayesian evolutionary algorithms for continuous function optimization, Shin, S.-Y. and Zhang, B.-T., Proceedings of the 2001 Congress on Evolutionary Computation (CEC2001), 1:508-515, 2001. [PDF]
  • Actively searching for committees of RBF networks using Bayesian evolutionary computation, Joung, J.-G. and Zhang, B.-T., Proceedings of the 2001 Congress on Evolutionary Computation (CEC2001), 1:372-377, 2001. [PDF]
  • Convergence properties of Bayesian evolutionary algorithms with population size greater than 1, Lee, S.-E., Zhang, B.-T., and Doucet, A., Proceedings of the 2001 Congress on Evolutionary Computation (CEC2001), 1:326-331, 2001. [PDF]
  • Function optimization with latent variable models, Shin, S.-Y., Cho, D.-Y., and Zhang, B.-T., Proceedings of the Third International Symposium on Adaptive Systems (ISAS2001), pp. 145-152, 2001. [PS]
  • A unified Bayesian framework for evolutionary learning and optimization, Zhang, B.-T., Advances in Evolutionary Computing, Chapter 15, pp. 393-412, Springer-Verlag, 2003. [PDF]
  • Bayesian methods for efficient genetic programming, Zhang, B.-T., Genetic Programming and Evolvable Machines, 1(3):217-242, 2000. [PDF]
  • Bayesian evolutionary optimization using Helmholtz machines, Zhang, B.-T. and Shin, S.-Y., Lecture Notes in Computer Science, 1917:827-836, 2000. [PS]
  • Building optimal committees of genetic programs, Zhang, B.-T. and Joung, J.-G., Lecture Notes in Computer Science, 1917:231-240, 2000. [PS]
  • Bayesian evolutionary algorithms for evolving neural tree models of time series data, Cho, D.-Y. and Zhang, B.-T., Proceedings of the 2000 Congress on Evolutionary Computation (CEC00), 2:1451-1458, 2000. [PDF]
  • Convergence properties of incremental Bayesian evolutionary algorithms with single Markov chains, Zhang, B.-T., Paaß, G., and Mühlenbein, H., Proceedings of the 2000 Congress on Evolutionary Computation (CEC00), vol. 2, pp. 938-945, 2000. [PDF]
  • Bayesian evolutionary algorithms for learning and optimization, Zhang, B.-T., Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000) Workshop Program, pp. 220-222, 2000. [PS]
  • Evolving neural trees for time series prediction using Bayesian evolutionary algorithms, Zhang, B.-T. and Cho, D.-Y., Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (ECNN2000), pp. 17-23, 2000. [PDF]
  • Bayesian genetic programming, Zhang, B.-T., Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'99) Workshop Program, pp. 68-70, 1999. [PS]
  • A Bayesian framework for evolutionary computation, Zhang, B.-T., Proceedings of the 1999 Congress on Evolutionary Computation (CEC99), 1:722-728, 1999. [PDF]

 

Incremental Data Inheritance

  • Concurrent evolution of neural networks and their data Sets, Joung, J.-G. and Zhang, B.-T., Proceedings of the 8th International Conference on Neural Information Processing (ICONIP-2001), 1:115-120, 2001. [PDF]
  • Genetic programming with active data selection, Zhang, B.-T. and Cho, D.-Y., Lecture Notes in Artificial Intelligence, 1585:146-153, 1999. [PDF]
  • Genetic programming with incremental data inheritance, Zhang, B.-T. and Joung, J.-G., Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'99), 2:1217-1224, Morgan Kaufmann, 1999. [PS]
  • Efficient model induction by a Bayesian evolutionary algorithm with incremental data inheritance, Zhang, B.-T. and Joung, J.-G., IEEE Transactions on Evolutionary Computation, 1998. (submitted)
  • Genetic programming with active data selection, Zhang, B.-T. and Cho, D.-Y., Lecture Notes in Artificial Intelligence, 1585:146-153, 1999. [PDF]

 

Adaptive Occam Method/Genetic Programming

  • Evolutionary induction of sparse neural trees, Zhang, B.-T., Ohm, P., and Mühlenbein, H., Evolutionary Computation, 5(2):213-236, 1997. [PS]
  • A Taxonomy of control schemes for genetic code growth, Zhang, B.-T., Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA-97) Workshop on Evolutionary Computation with Variable Size Representation, 1997.
  • Adaptive fitness functions for dynamic growing/pruning of program trees, Zhang, B.-T. and Mühlenbein., H., Advances in Genetic Programming, vol. 2, Chapter 12, pp. 241-256, MIT Press, 1996.
  • Balancing accuracy and parsimony in genetic programming, Zhang, B.-T. and Mühlenbein, H., Evolutionary Computation, vol. 3, no. 1, pp. 17-38, 1995.
  • Bayesian inference, minimum description length principle and learning by genetic programming, Zhang, B.-T. and Mühlenbein, H., Proceedings of the 12th International Conference on Machine Learning (ICML'95) Workshop on Genetic Programming, pp. 1-5, 1995.
  • MDL-based fitness functions for learning parsimonious programs, Zhang, B.-T. and Mühlenbein, H., Proceedings of the 1995 AAAI Fall Symposium on Genetic Programming, pp. 122-126, AAAI Press, 1995.
  • Effects of Occam's razor in evolving sigma-pi neural nets, Zhang, B.-T., Lecture Notes in Computer Science, 866:462-471, 1994.
  • Evolving optimal neural networks using genetic algorithms with Occam's razor, Zhang, B.-T. and Mühlenbein, H., Complex Systems, 7(3):199-220, 1993. [PDF]
  • Genetic programming of minimal neural nets using Occam's razor, Zhang, B.-T. and Mühlenbein, H., Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA-93), pp. 342-349, Morgan Kaufmann, 1993. [PDF]

 

Evolvable Hardware

  • Behavior evolution of autonomous mobile robot (AMR) using genetic programming based on evolvable hardware, Sim, K.-B., Lee, D.-W., and Zhang, B.-T., International Journal of Fuzzy Logic and Intelligent Systems, 2(1):20-25, 2002. [PDF]
  • Evolutionary calibration of sensors using genetic programming on evolvable hardware, Seok, H.-S. and Zhang, B.-T., Proceedings of the 2001 Congress on Evolutionary Computation (CEC2001), 1:630-634, 2001. [PDF]
  • Behavior evolution of autonomous mobile robot using genetic programming based on evolvable hardware, Lee, D.-W., Ban, C.-B., Sim, K.-B., Seok, H.-S., Lee, K.-J., and Zhang, B.-T., Proceedings of the 2000 IEEE International Conference on Systems, Man, and Cybernetics (SMC2000), 5:3835-3840, 2000. [PDF]
  • Genetic programming of process decomposition strategies for evolvable hardware, Seok, H.-S., Lee, K.-J., Zhang, B.-T., Lee, D.-W., and Sim, K.-B., Proceedings of the Second NASA/DoD Workshop on Evolvable Hardware (EH-2000), pp. 25-34, 2000. [PDF]
  • An on-line learning method for object-locating robots using genetic programming on evolvable hardware, Seok, H.-S., Lee, K.-J., Joung, J.-G., and Zhang, B.-T., Proceedings of the Fifth International Symposium on Artificial Life and Robotics (AROB'00), 1:321-324, 2000. [PS]

 

Selection

  • Comparison of selection methods for evolutionary optimization, Zhang, B.-T. and Kim, J.-J., Evolutionary Optimization, 2(1):55-70, 2000. [PDF]
  • Effects of selection schemes in genetic programming for time series analysis, Kim, J.-J. and Zhang, B.-T., Proceedings of the 1999 Congress on Evolutionary Computation (CEC99), 1: 252-258, 1999. [PDF]
  • Comparison of selection schemes for machine layout design, Kim, J.-J. and Zhang, B.-T., Proceedings of the Second Asia Pacific Conference on Simulated Evolution and Learning (SEAL'98), vol. 2, 1998.

 

Coevolution

  • Enhancing robustness of genetic programming at the species level, Zhang, B.-T. and Joung, J.-G., Proceedings of the Second Annual Genetic Programming Conference (GP-97), pp. 336-342, Morgan Kaufmann, 1997.

 

Optimization

  • Using a genetic algorithm for communication link partitioning, Lee, J.-H., Choi, Y.-H., Zhang, B.-T., and Kim, C.-S., Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC'97), pp. 581-584, 1997. [PDF]

 

Evolving Neural Trees

  • Evolutionary neural trees for modeling and predicting complex systems, Zhang, B.-T., Ohm, P., and Mühlenbein, H., Engineering Applications of Artificial Intelligence, 10(5):473-483, 1997. [PDF]
  • Water pollution prediction with evolutionary neural trees, Zhang, B.-T., Ohm, P., and Mühlenbein, H., Proceedings of the 14th Internationl Joint Conference on Artificial Intelligence (IJCAI-95) Workshop on AI and the Environment, 1995.
  • Learning to predict by evolutionary neural trees, Zhang, B.-T., Ohm, P., and Mühlenbein, H., Proceedings of the World Congress on Neural Networks (WCNN'95), 1:823-826, 1995.
  • Using genetic algorithms for automatic construction of higher-order neural models, Zhang, B.-T. and Mühlenbein, H., Proceedings of the First International Conference on Neural Information Processing (ICONIP'94), vol. 1, pp. 168-173, 1994. [PDF]
  • Synthesis of sigma-pi neural networks by the breeder genetic programming, Zhang, B.-T. and Mühlenbein, H., Proceedings of the First IEEE Conference on Evolutionary Computation (ICEC'94), 1:318-323, 1994. [PDF]
  • Genetic breeding of novel neural architectures, Zhang, B.-T. and Mühlenbein, H., Proceedings of the Second European Congress on Intelligent Techniques and Soft Computing, pp. 1265-1269, 1994.

This page is maintained by Byoung-Hee Kim
Last update: February 7, 2014.