DEMO Publications and Technical Reports

Title Index

• Pollack, Jordan B., Lipson, Hod, Ficici, Sevan G., Funes, Pablo, Hornby, Greg and Watson, Richard A. (2001). .
  "Evolutionary Techniques in Physical Robots," in Creative Evolutionary Systems, Peter J. Bently and David W. Corne (eds). Morgan-Kaufmann, 2001.

• De Jong, E.D. and Oates, T. (2002). A Coevolutionary Approach to Representation Development .
  Proceedings of the ICML-2002 Workshop on Development of Representations.

• Watson, R.A. and Pollack, J.B. (2002). A Computational Model of Symbiotic Composition in Evolutionary Transitions (PREPRINT) .
  Biosystems, Special Issue on Evolvability, (preprint 2001 - to appear 2002) .

• Darwen, P. J. and Yao, X. (1995). A Dilemma for Fitness Sharing with a Scaling Function.
  Second IEEE International Conference on Evolutionary Computation.

• Sklar, Elizabeth and Pollack, Jordan (2000). A Framework for Enabling an Internet Learning Community.
  Educational Technology & Society 3(3), Kinshuk, A. Patel and R. Oppermann (eds.).

• Ficici, Sevan G., Melnik, Ofer and Pollack, Jordan B. (2005). A Game-Theoretic and Dynamical-Systems Analysis of Selection Methods in Coevolution.
  IEEE Transactions on Evolutionary Computation 9(6), 580-602.

• Ficici, Sevan G. and Pollack, Jordan B. (2000). A Game-Theoretic Approach to the Simple Coevolutionary Algorithm.
  Parallel Problem Solving from Nature VI, M. Schoenauer, et al, (eds.), Springer Verlag, 2000.

• Ficici, Sevan G., Melnik, Ofer and Pollack, Jordan B. (2000). A Game-Theoretic Investigation of Selection Methods Used in Evolutionary Algorithms.
  Proceedings of the 2000 Congress on Evolutionary Computation, A. Zalzala, et al, (eds.), IEEE Press, 2000.

• Ficici, Sevan G. and Pollack, Jordan B. (2003). A Game-Theoretic Memory Mechanism for Coevolution.
  Proc. 2003 Genetic and Evolutionary Computation Conference, Cantú-Paz, et al (eds.). Springer, 2003 pp. 286--297.

• Burjorjee, K. and Pollack, J.B, (2006). A General Coarse-Graining Framework for Studying Simultaneous Inter-Population Constraints Induced by Evolutionary Operations.
  Proceedings of the 2006 Genetic and Evolutionary Computation Conference, ACM Press, 2006.

• Melnik, O. and Pollack, J.B. (1998). A Gradient Descent Method for a Neural Fractal Memory.
  WCCI 98 (IJCNN), IEEE, Received "Best Student Paper Award".

• Bucci, A. and Pollack, J.B. (2002). A Mathematical Framework for the Study of Coevolution (prepublication).
  Foundations of Genetic Algorithms 7. Proceedings of FOGA VII, Torremolinos Spain, 4-6 September 2002 (prepublication version -- to appear spring 2003).

• Bader-Natal, A. and Pollack, J. (2004). A Population-Differential Method of Monitoring Success and Failure in Coevolution.
  Proceedings of the 2004 Genetic and Evolutionary Computation Conference, Springer Verlag, 2004.

• Juillé, H. and Pollack, J. B. (1998). A Sampling-Based Heuristic for Tree Search Applied to Grammar Induction.
  Proceedings of the Fifteenth National Conference on Artificial Intelligence , Madison, Wisconsin, July 26 - 30, 1998.

• Mou, Z. George and Ficici, Sevan G. (1995). A Scalable Divide-and-Conquer Parallel Algorithm for Finite State Automata and Its Applications.
  Proceedings of the 7th SIAM Conference on Parallel Processing for Scientific Computing. Philadelphia: Society for Industrial and Applied Mathematics, 1995.

• Juillé, H. and Pollack, J. B. (1998). A Stochastic Search Approach to Grammar Induction.
  Proceedings of the Fourth International Colloquium on Grammar Inference , Ames, Iowa, July 12 - 14, 1998.

• Saunders, G., Kolen, J, Angeline, P and Pollack, J. (1992). Additive Modular learning in Preemptrons.
  14th Annual Cognitive Science Conference, 1098-1103.

• Ficici, S.G. and Bucci, A. (2007). Advanced Tutorial on Coevolution.
  Presented at the 2007 Genetic and Evolutionary Computation Conference, London, UK.

• Rieffel, J. and Pollack, J. (2006). An Endosymbiotic Model for Modular Acquisition in Stochastic Developmental Systems.
  Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems (ALIFE X).

• Angeline, P., Saunders, G and Pollack, J. (1994). An evolutionary algorithm that constructs recurrent networks.
  IEEE Trans. on Neural Networks, 5, 54-65.

• Sklar, Elizabeth and Pollack, Jordan (2000). An evolutionary approach to guiding students in an educational game.
  In From Animals to Animats 6: Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior. Meyer, J. et. al (eds.) MIT Press.

• Blair, A.D. and Pollack, J.B. (1997). Analysis of Dynamical Recognizers.
  Neural Computation 9(5), 1127-1142.

• Watson, R.A. (2001). Analysis of Recombinative Algorithms on a Non-Separable Building-Block Problem.
  Foundations of Genetic Algorithms, Volume 6 , proceedings of FOGA VI, Charlottesville, VA, July 21-23, 2000, Edited by Worthy N. Martin and William M. Spears, Morgan Kaufmann.

• Funes, P., Sklar, E., Juillé, H. and Pollack, J. (1998). Animal-Animat Coevolution: Using the Animal Population as Fitness Function.
  Pfeifer, R. et. al. (eds.) From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior . MIT Press. pp 525-533.

• Rieffel, J. and Pollack, J. (2004). Artificial Ontogenies for Real World Design and Assembly.
  Ninth International Conference on the Simulation and Synthesis of Living Systems (ALIFE9) Workshop: Self-Organization and Development in Artificial and Natural Systems (SODANS) 2004.

• Bader-Natal, A. and Pollack, J.B. (2007). Assessing Learning in a Peer-Driven Tutoring System.
  Proceedings of the 13th International Conference on Artificial Intelligence in Education, IOS Press, 2007.

• Harrington, K. I., Spector, L., Pollack, J. B. and O'Reilly, U.M. (2012). Autoconstructive Evolution for Structural Problems.
  Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion , pp.75-82.

• Rieffel, J. and Pollack, J. (2005). Automated Assembly as Situated Development: Using Artificial Ontogenies to Evolve Buildable 3-D Objects .
  Proceedings of the 2005 Genetic and Evolutionary Computation Conference .

• Bucci, A., Pollack, J.B. and De Jong, E.D. (2004). Automated Extraction of Problem Structure.
  Proceedings of the 2004 Genetic and Evolutionary Computation Conference, Springer Verlag, 2004.

• Darwen, P. J. and Yao, X. (1996). Automatic Modularization by Speciation.
  Third IEEE International Conference on Evolutionary Computation.

• Hornby, G.S., Fujita, M., Takamura, S., Yamamoto, T. and Hanagata, O. (1999). Autonomous Evolution of Gaits with the Sony Quadruped Robot.
  Proceedings of 1999 Genetic and Evolutionary Computation Conference (GECCO). Banzhaf, Daida, Eiben, Garzon, Honavar, Jakiela, Smith, eds., Morgan Kauffmann, pp. 1297-1304.

• Kolen, J. F. and Pollack, J. B. (1990). Back-Propagation is Sensitive to Initial Conditions .
  Complex Systems, 4,3, 269-280.

• Bader-Natal, A. and Pollack, J.B. (2006). BEEweb: A Multi-Domain Platform for Reciprocal Peer-Driven Tutoring Systems.
  Proceedings of the 8th International Conference on Intelligent Tutoring Systems, Springer, 2006.

• Hornby, Gregory. S. and Pollack, Jordan. B. (2001). Body-Brain Co-evolution Using L-systems as a Generative Encoding.
  Genetic and Evolutionary Computation Conference.

• Ficici, Sevan G. and Pollack, Jordan B. (1998). Challenges in Coevolutionary Learning: Arms-Race Dynamics, Open-Endedness, and Mediocre Stable States.
  Proceedings of the Sixth International Conference on Artificial Life. Adami, Belew, Kitano, Talor, eds. Cambridge: MIT Press, 1998.

• Sklar, Elizabeth, Johnson, Jeffrey and Lund, Henrik Hautop (2000). Children Learning From Team Robotics: RoboCup Junior 2000.
  Educational Research Report, Department of Design and Innovation, Faculty of Technology, The Open University, Milton Keynes, UK.

• Pollack, Jordan. B. and Blair, Alan D. (1998). Co-Evolution in the Successful Learning of Backgammon Strategy.
  Machine Learning 32(3): 225-240, September 1998.

• Pollack, J.P. and Blair, A.D. (1997). Co-Evolution in the Successful Learning of Backgammon Strategy.
  Brandeis University Computer Science Technical Report CS-97-193.

• Blair, Alan D. , Sklar, Elizabeth and Funes, Pablo (1998). Co-evolution, Determinism and Robustness.
  In Simulated Evolution and Learning (SEAL-98). Lecture Notes in Artificial Intelligence 1585. Bob McKay, Xin Yao, Charles S. Newton, Jong-Hwan Kim, Takeshi Furahashi, eds., Springer-Verlag.

• Sklar, E., Blair, A.D. and Pollack, J.B. (1998). Co-evolutionary Learning: Machines and Humans Schooling Together.
  Workshop on Current Trends and Applications of Artificial Intelligence in Education: Fourth World Congress on Expert Systems.

• Juillé, H. and Pollack, J. B. (1996). Co-evolving Intertwined Spirals.
  Proceedings of the Fifth Annual Conference on Evolutionary Programming, San Diego, CA, February 29 - March 2, 1996, MIT Press, pp. 461-468.

• Pollack, J, Blair, A. and Land, M. (1996). Coevolution of A Backgammon Player.
  Proceedings Artificial Life V, C. Langton, (Ed), MIT Press.

• Watson, R.A. and Pollack, J.B. (2001). Coevolutionary Dynamics in a Minimal Substrate.
  Proceedings of the 2001 Genetic and Evolutionary Computation Conference, Spector, L, et al (eds.), Morgan Kaufmann, 2001.

• Juillé, H. and Pollack, J. B. (1998). Coevolutionary Learning: a Case Study.
  Proceedings of the Fifteenth International Conference on Machine Learning, Madison, Wisconsin, July 24 - 26, 1998.

• Pollack, Jordan B., Lipson, Hod, Funes, Pablo, Ficici, Sevan G. and Hornby, Greg (1999). Coevolutionary Robotics.
  The First NASA/DoD Workshop on Evolvable Hardware (EH'99). John R. Koza, Adrian Stoica, Didier Keymeulen, Jason Lohn, eds., IEEE Press.

• Ficici, Sevan G. and Pollack, Jordan B. (1998). Coevolving Communicative Behavior in a Linear Pursuer-Evader Game.
  Proceedings of the Fifth International Conference of the Society for Adaptive Behavior. Pfeifer, Blumberg, Kobayashi, eds. Cambridge: MIT Press, 1998.

• Angeline, P. J. and Pollack, J. B. (1994). Coevolving High-Level Representations. .
  Artificial Life III, C. Langton (ed.), Addison-Wesley: Reading MA, pp. 55-71. .

• Juillé, H. and Pollack, J. B. (1998). Coevolving the "Ideal" Trainer: Application to the Discovery of Cellular Automata Rules.
  Proceedings of the Third Annual Genetic Programming Conference , Madison, Wisconsin, July 22 - 25, 1998.

• Harrington, K., Olsen, M. and Siegelmann, H. (2011). Communicated Somatic Markers Benefit the Individual and the Species.
  Proceedings of the International Joint Conference on Neural Networks, pp.3272-3278.

• Angeline, P. J. and Pollack, J. B. (1993). Competitive environments evolve better solutions to complex problems.
  Fifth International Conference on Genetic Algorithms. 264-270.

• Angeline, P. J., Saunders, G. M. and Pollack, J. B. (1994). Complete Induction of Recurrent Neural Networks,.
  Proceedings of the Third International Conference on Evolutionary Programming.

• Funes, P. J. and Pollack, J. B. (1998). Componential Structural Simulator.
  Brandeis University Department of Computer Science Technical Report CS-98-198.

• Harrington, K., Tosch, E., Spector, L. and Pollack, J. (2011). Compositional Autoconstructive Dynamics.
  Unifying Themes in Complex Systems Volume VIII: Proceedings of the Eighth International Conference on Complex Systems, pp.856-870.

• Olsen, M., Harrington, K. and Siegelmann, H. (2011). Computational Emotions in a Population Dynamics Cellular Automata Encourage Collective Behavior.
  Unifying Themes in Complex Systems Volume VIII: Proceedings of the Eighth International Conference on Complex Systems, pp.196-210.

• Harrington, K. I., Olsen, M. and Siegelmann, H. (2012). Computational Neuroecology of Communicated Somatic Markers.
  Proceedings of Artificial Life XIII, pp.555-556.

• Brinkman, Alexander and Ficici, Sevan G. (1992). Computational Tools for the Analysis of Rhythm.
  Proceedings of the 1992 International Computer Music Conference. San Francisco: International Computer Music Association, 1992.

• Ficici, Sevan G. (1991). Computer Analysis of Surface Detail in Tonal Music.
  Proceedings of the 1991 International Computer Music Conference. San Francisco: International Computer Music Association, 1991.

• Funes, P. and Pollack, J. (1997). Computer Evolution of Buildable Objects.
  Fourth European Conference on Artificial Life, P. Husbands and I. Harvey, eds., MIT Press 1997. pp 358-367.

• Funes, P. and Pollack, J. (1999). Computer Evolution of Buildable Objects.
  In Evolutionary Design by Computers. P. Bentley (editor). Morgan Kaufmann, San Francisco. pp. 387-403.

• Pollack, J. B. (1989). Connectionism: Past, Present, and Future.
  Artificial Intelligence Review, 3, 3-20.

• Olsen, M., Harrington, K. and Siegelmann, H. (2010). Conspecific Emotional Cooperation Biases Population Dynamics: A Cellular Automata Approach.
  International Journal of Natural Computing Research 3(1), pp.51-65.

• Peshkin, L. and De Jong, E.D. (2002). Context-based policy search: transfer of experience across problems.
  Proceedings of the ICML-2002 Workshop on Development of Representations.

• Hornby, Gregory S. and Pollack, Jordan. B. (2002). Creating High-Level Components with a Generative Representation for Body-Brain Evolution.
  Artificial Life, 8:3.

• Rieffel, J. and Pollack, J.B. (2005). Crossing the Fabrication Gap: Evolving Assembly Plans to Build 3-D Objects.
  2005 IEEE Congress on Evolutionary Computation.

• De Jong, E.D. and Bucci, A. (2006). DECA: Dimension Extracting Coevolutionary Algorithm.
  Proceedings of the 2006 Genetic and Evolutionary Computation Conference, ACM Press, 2006.

• Melnik, O. (2002). Decision Region Connectivity Analysis: A method for analyzing high-dimensional classifiers.
  Machine Learning 48:(1/2/3) .

• Lowell, J. H. and Pollack, J. B. (2016). Developmental Encodings Promote the Emergence of Hierarchical Modularity.
  Proceedings of Artificial Life Conference, Cancun Mx, 344-351.

• Hornby, G.S. and Mirtich, B. (1999). Diffuse versus True Coevolution in a Physics-based World.
  Proceedings of 1999 Genetic and Evolutionary Computation Conference (GECCO). B\anzhaf, Daida, Eiben, Garzon, Honavar, Jakiela, Smith, eds., Morgan Kauffmann, pp. 1305-1312.

• Juillé, H. and Pollack, J. B. (1996). Dynamics of Co-evolutionary Learning.
  Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, Cape Cod, MA, September 9 - 13, 1996, MIT Press, pp. 526-534.

• Moran, N. E. and Pollack, J. B. (2017). Effects of Cooperative and Competitive Coevolution on Complexity in a Linguistic Prediction Game.
  Proceedings of European Conference on Artificial Life, Lyon France, 298-305.

• Ficici, Sevan G. and Pollack, Jordan B. (2000). Effects of Finite Populations on Evolutionary Stable Strategies.
  Proceedings of the 2000 Genetic and Evolutionary Computation Conference, L. Darrell Whitley (ed.), Morgan-Kaufmann, 2000.

• Ficici, Sevan G., Watson, Richard A. and Pollack, Jordan B. (1999). Embodied Evolution: A Response to Challenges in Evolutionary Robotics.
  Eighth European Workshop on Learning Robots. Jeremy L. Wyatt, John Demiris, eds., 14-22.

• Watson, Richard A., Ficici, Sevan G. and Pollack, Jordan B. (2002). Embodied Evolution: Distributing an Evolutionary Algorithm in a Population of Robots.
  Robotics and Autonomous Systems 39/1 (2002) 1-18.

• Watson, Richard A., Ficici, Sevan G. and Pollack, Jordan B. (2000). Embodied Evolution: Distributing an Evolutionary Algorithm in a Population of Robots.
  Technical Report CS-00-208.

• Watson, Richard A., Ficici, Sevan G. and Pollack, Jordan B. (1999). Embodied Evolution: Embodying an Evolutionary Algorithm in a Population of Robots.
  1999 Congress on Evolutionary Computation. Angeline, Michalewicz, Schoenauer, Yao, Zalzala, eds. IEEE Press, 335-342.

• Bucci, A. (2007). Emergent Geometric Organization and Informative Dimensions in Coevolutionary Algorithms.
  PhD dissertation, Michtom School of Computer Science, Brandeis University.

• Heymann, M., Harrington, K. I., Pollack, J. and Fraden, S. (2010). En route to signal inversion in chemical computing.
  Proceedings of Artificial Life XII, pp.166-167.

• Levy, Simon D. and Pollack, Jordan B. (2003). Escape the Building-Block / Rule Dichotomy: A Case Study.
  AAAI Spring Symposium on Computational Synthesis.

• Bader-Natal, A. and Pollack, J.B. (2007). Evaluating Problem Difficulty Rankings Using Sparse Student Response Data.
  Supplementary Proceedings of the 13th International Conference on Artificial Intelligence in Education, IOS Press, 2007.

• Funes, Pablo, Lapat, Louis and Pollack, Jordan B. (2000). EvoCAD: Evolution-Assisted Design.
  Artificial Intelligence in Design'00 (Poster Abstracts) Key Centre of Design Computing and Cognition, University of Sidney. pp 21-24.

• Funes, Pablo (2001). Evolution of Complexity in Real-World Domains.
  Brandeis University Dept. of Computer Science PhD Dissertation.

• Hornby, G.S., Takamura, S., Yokono, J., Hanagata, O., Fujita, M. and Pollack, J. (2000). Evolution of Controllers from a High-Level Simulator to a High DOF Robot.
  Miller, J. (ed) Evolvable Systems: from biology to hardware; proceedings of the third international conference (ICES 2000). Springer (Lecture Notes in Computer Science; Vol. 1801). pp. 80-89.

• Hornby, Gregory. S., Lipson, Hod and Pollack, Jordan. B. (2001). Evolution of Generative Design Systems for Modular Physical Robots.
  IEEE International Conference on Robotics and Automation.

• Juillé, H. (1995). Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces.
  Proceedings of the Sixth International Conference on Genetic Algorithms, Pittsburgh, PA, July 1995, pp. 351-358.

• Funes, P. and Pollack, J. (1998). Evolutionary Body Building: Adaptive physical designs for robots.
  Artificial Life 4: 337-357.

• Rieffel, J. (2006). Evolutionary Fabrication: The Co-Evolution of Form and Formation .
  PhD Dissertation, Brandeis University, May 2006.

• Rieffel, J. and Pollack, J.B. (2005). Evolutionary Fabrication: The Emergence of Novel Assembly Methods in Artificial Ontogenies .
  SEEDS workshop, at the 2005 Genetic and Evolutionary Computation Conference.

• Angeline, P. J. and Pollack, J. B (1992). Evolutionary Induction of subroutines. .
  14th Annual Cognitive Science Conference, 236-241.

• Angeline, P. J. and Pollack, J. B. (1993). Evolutionary Module Acquisition.
  Second Evolutionary Programming Meeting, 154-163.

• Pollack, J. B., Lipson. H., , Ficici, S., Funes, P., Hornby, G. and Watson, R. (2000). Evolutionary Techniques in Physical Robotics.
  Miller, J. (ed) Evolvable Systems: from biology to hardware; proceedings of the third international conference (ICES 2000). Springer (Lecture Notes in Computer Science; Vol. 1801). pp. 175-186.

• Rieffel, J. and Pollack, J. (2005). Evolving Assembly Plans for Fully Automated Design and Assembly.
  Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware .

• Hornby, Gregory S. and Pollack, Jordan. B. (2001). Evolving L-Systems To Generate Virtual Creatures.
  Computers and Graphics, 25:6, pp 1041-1048.

• Juillé, H. (1994). Evolving Non-Determinism: An Inventive and Efficient Tool for Optimization and Discovery of Strategies.
  Technical Report.

• Hornby, G.S., Takamura, S., Yokono, J., Hanagata, O., Yamamoto, T. and Fujita, M. (2000). Evolving Robust Gaits with AIBO.
  IEEE International Conference on Robotics and Automation. pp. 3040-3045.

• Melnik, O. and Pollack, J.B. (2000). Exact Representations from Feed-Forward Networks .
  IJCNN 2000, IEEE press, shorter conference version of some of tech-report .

• Pollack, J.B (1987). (Excerpt) On Connectionist Models of Natural Language Processing.
  Ph.D Dissertation in Computer Science, University of Illinois.

• Blair, Alan D. and Sklar, Elizabeth (1999). Exploring evolutionary learning in a simulated hockey environment.
  1999 Congress on Evolutionary Computation. Peter J. Angeline, Zbyszek Michalewicz, Marc Schoenauer, Xin Yao, Ali Zalzala, eds. .

• Spector, L., Helmuth, T. and Harrington, K. (2011). Fecundity and Selectivity in Evolutionary Computation.
  Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, pp.

• Bucci, A. and Pollack, J.B. (2003). Focusing versus Intransitivity: Geometrical Aspects of Co-evolution.
  Proceedings of the 2003 Genetic and Evolutionary Computation Conference, Springer Verlag, 2003.

• Stucki, D. S. and Pollack, J. B. (1992). Fractal (Reconstructive Analogue) Memory.
  14th Annual Cognitive Science Conference, 118-123.

• Ficici, Sevan G. and Pollack, Jordan B. (2001). Game Theory and the Simple Coevolutionary Algorithm: Some Preliminary Results on Fitness Sharing.
  GECCO 2001 Workshop on Coevolution: Turning Adaptative Algorithms upon Themselves.

• Hornby, Gregory S. (2003). Generative Representations for Evolutionary Design Automation.
  Brandeis University Dept. of Computer Science, Ph.D. Dissertation.

• Watson, R.A. and Pollack, J.B. (1999). Hierarchically-Consistent Test Problems for Genetic Algorithms .
  Proceedings of 1999 Congress on Evolutionary Computation (CEC 99). Angeline, Michalewicz, Schoenauer, Yao, Zalzala, eds. IEEE Press, pp.1406-1413.

• Viswanathan, S. and Pollack, J.B. (2005). How Artificial Ontogenies can retard evolution.
  SEEDS workshop, 2005 Genetic and Evolutionary Computation Conference (GECCO 2005), ACM Press, 2005.

• Watson, R.A. and Pollack, J.B. (1999). How Symbiosis Can Guide Evolution .
  Fifth European Conference on Artificial Life. Dario Floreano, Jean-Daniel Nicoud, Francesco Mondada, eds. Springer, 1999.

• Watson, R.A. (2002). Compositional Evolution: Interdisciplinary Investigations in Evolvability, Modularity, and Symbiosis.
  PhD Dissertation, Brandeis University, May 2002.

• Melnik, O. (2000). Representation of Information in Neural Networks.
  PhD Dissertation, Brandeis University, October 2000.

• Ficici, Sevan G. (2004). Solution Concepts in Coevolutionary Algorithms.
  Ph.D Dissertation, Brandeis University, May 2004. (Computer Science Department Technical Report CS-03-243).

• Shang, Y., Haynes, P., Pírez, N., Harrington, K., Guo, F., Pollack, J., Hong, P., Griffith, L. and Rosbash, M. (2011). Imaging analysis of clock neurons: light buffers the wake-promoting effect of dopamine.
  Nature Neuroscience 7(14), pp.889-895.

• Pollack, J. B. (1989). Implications of Recursive Distributed Representations.
  Advances in Neural Information Processing Systems I, 527-536.

• Juillé, H. (1995). Incremental Co-evolution of Organisms: A New Approach for Optimization and Discovery of Strategies.
  Proceedings of the Third European Conference on Artificial Life, Granada, Spain, June 1995, pp.246-260.

• Watson, R.A. and Pollack, J.B. (1999). Incremental Commitment in Genetic Algorithms .
  Proceedings of 1999 Genetic and Evolutionary Computation Conference (GECCO 99). Banzhaf, Daida, Eiben, Garzon, Honavar, Jakiela, Smith, eds., Morgan Kauffmann, pp.710-717.

• Levy, S., Melnik, O. and Pollack, J.B. (2000). Infinite RAAM: A Principled Connectionist Basis for Grammatical Competence.
  COGSCII 2000, IEEE press .

• Levy, S. and Pollack, J. (2001). Infinite RAAM: A Principled Connectionist Substrate for Cognitive Modeling.
  ICCM2001, Lawrence Erlbaum Associates.

• Levy, Simon D. (2002). Infinite RAAM: Initial Investigations into a Fractal Basis for Cognition.
  Ph.D. Thesis, Brandeis University, July 2002.

• Ficici, S.G. and Bucci, A. (2006). Introductory Tutorial on Coevolution.
  Presented at the 2006 Genetic and Evolutionary Computation Conference, Seattle, WA, USA.

• De Jong, Edwin D. and Pollack, Jordan B. (2003). Learning the Ideal Evaluation Function.
  Proceedings of GECCO 2003, pp. 277-288.

• Levy, S. and Pollack, J.B. (2001). Logical Computation on a Fractal Neural Substrate.
  IJCNN 2001, IEEE press .

• Juillé, H. and Pollack, J. (1996). Massively Parallel Genetic Programming.
  Advances in GP II, Kinnear & Angeline, Ed. MIT Press.

• Funes, Pablo and Pollack, Jordan B. (2000). Measuring Progress in Coevolutionary Competition.
  From Animals to Animats 6: Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior. Meyer, J. et. al (eds.) MIT Press. Pp. 450-459.

• Juillé, H. (1999). Methods for Statistical Inference: Extending the Evolutionary Computation Paradigm.
  Doctoral Dissertation, Brandeis University, Department of Computer Science, May 1999.

• Pollack, J. (2006). Mindless Intelligence.
  IEEE Trans. on Intelligent Systems, 21, 3, 50-56.

• Watson, Richard A. , Hornby, G. S. and Pollack, J. B. (1998). Modeling Building-Block Interdependency .
  Parallel Problem Solving from Nature, proceedings of Fifth International Conference /PPSN V, Springer 1998, pp.97-106 .

• Ficici, Sevan G. (2005). Monotonic Solution Concepts in Coevolution.
  Proc. 2005 Genetic and Evolutionary Computation Conference, H.-G. Beyer, et al (eds.). ACM Press, 2005 pp. 499--506. (content identical to definitive ACM version.).

• Bader-Natal, A. and Pollack, J.B. (2005). Motivating Appropriate Challenges in a Reciprocal Tutoring System.
  Proceedings of the 12th International Conference on Artificial Intelligence in Education, IOS Press, 2005.

• Kolen, J. F. and Pollack, J. B (1991). Multi-Associative Memory.
  13th Annual Cognitive Science Conference, 785-789.

• Watson, R.A. , Reil, T. and Pollack, J.B. (2000). Mutualism, Parasitism, and Evolutionary Adaptation.
  Proceedings of Artificial Life VII, Bedau, M, McCaskill, J, Packard, N, Rasmussen, S (eds.), 2000.

• Shipman, R., Shackleton, M., Ebner, M. and Watson, R.A. (2000). Neutral Search Spaces for Artificial Evolution: A Lesson from Life.
  Proceedings of Artificial Life VII, Bedau, M, McCaskill, J, Packard, N, Rasmussen, S (eds.), 2000.

• Pollack, J. B. (1989). No Harm Intended: A Review of the Perceptrons expanded edition. .
  Journal of Mathematical Psychology, 33, 3, 358-365. .

• De Jong, E.D. and Bucci, A. (2008). Objective Set Compression: Test-based Problems and Multi-objective Optimization.
  Chapter in Multi-Objective Problem Solving from Nature: From Concepts to Applications. Joshua Knowles et al. (editors). Springer-Verlag.

• Bucci, A. and Pollack, J.B. (2005). On Identifying Global Optima in Cooperative Coevolution.
  Proceedings of the 2005 Genetic and Evolutionary Computation Conference, ACM Press, 2005.

• Viswanathan, S. and Pollack, J.B. (2005). On the coevolutionary construction of learnable gradients.
  Coevolutionary and Coadaptive Systems, AAAI Fall Symposium 2005, Washington D.C.

• Viswanathan, S. and Pollack, J.B. (2005). On the robustness achievable with stochastic development processes .
  The 2005 NASA/DoD Conference on Evolvable Hardware, IEEE Press, 2005 .

• Pollack, J. B. (1992). On Wings of Knowledge: A review of Newell's Unified Theories of Cognition.
  Artificial Intelligence, 59, 355-369.

• Bucci, A. and Pollack, J.B. (2002). Order-theoretic Analysis of Coevolution Problems: Coevolutionary Statics.
  2002 Genetic and Evolutionary Computation Conference Workshop: Understanding Coevolution.

• Noble, J. and Watson, R.A. (2001). Pareto coevolution: Using performance against coevolved opponents in a game as dimensions for Pareto selection .
  In Spector, L., Goodman, E., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M., & Burke, E. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001, pp. 493-500. Morgan Kauffman, San Francisco. .

• Ficici, Sevan G. and Pollack, Jordan B. (2001). Pareto Optimality in Coevolutionary Learning.
  Advances in Artificial Life: 6th European Conference (ECAL 2001)J. Kelemen, P. Sosik (eds.), Springer, 2001.

• Melnik, O., Levy, S. and Pollack, J.B. (2000). RAAM for Infinite Context-Free Languages.
  IJCNN 2000, IEEE press .

• Watson, R.A. and Pollack, J.B. (2000). Recombination Without Respect: Schema Combination and Disruption in Genetic Algorithm Crossover.
  Proceedings of the 2000 Genetic and Evolutionary Computation Conference, Whitley, D., et al (eds.), Morgan Kaufmann, 2000.

• Pollack, J. B. (1990). Recursive Distributed Representations.
  Artificial Intelligence 46, 1, 77-105.

• Large, E. W., Palmer, C and Pollack, J (1995). Reduced Memory Representations for Music.
  Cognitive Science, 19,1,53-96.

• De Jong, Edwin D., Watson, Richard A. and Pollack, Jordan B. (2001). Reducing Bloat and Promoting Diversity using Multi-Objective Methods.
  Proceedings of GECCO 2001.

• Harrington, K., Awa, E., Cussat-Blanc, S. and Pollack, J. (2013). Robot Coverage Control by Evolved Neuromodulation.
  Proceedings of the International Joint Conference on Neural Networks, pp.543-550.

• Harrington, K. I. and Pollack, J. B. (2010). Robot Phylogenetics.
  Proceedings of the 12th annual conference companion on Genetic and evolutionary computation, pp.2077-2078.

• Moran, N. E. and Pollack, J. B. (2016). Route Assignment for Autonomous Vehicles.
  10th Conference on Swarm Intelligence, Brussels Belgium 265-272.

• Blair, A.D. (1997). Scaling Up RAAMs.
  Brandeis University Computer Science Technical Report CS-97-192.

• Ficici, Sevan G., Melnik, Ofer and Pollack, Jordan B. (2004). Selection in Coevolutionary Algorithms and the Inverse Problem.
  Collectives and the Design of Complex Systems, Kagan Tumer and David Wolpert (eds.). Springer, 2004 pp. 277-294.

• Ficici, Sevan G. and Pollack, Jordan B. (1999). Statistical Reasoning Strategies in the Pursuit and Evasion Domain.
  Fifth European Conference on Artificial Life. Dario Floreano, Jean-Daniel Nicoud, Francesco Mondada, eds. Springer, 1999.

• Saunders, G., Angeline, P. and Pollack, J. B. (1994). Structural and Behavioral Evolution of Recurrent Networks.
  Neural Information Processing Systems Conference 6.

• Watson, R.A. and Pollack, J.B. (2000). Symbiotic Combination as an Alternative to Sexual Recombination in Genetic Algorithms.
  Proceedings of Parallel Problem Solving from Nature (PPSNVI), Marc Schoenauer, Kalyanmoy Deb, Guenter Rudolph, Xin Yao, Evelyne Lutton, Juan Julian Merelo, Hans-Paul Schwefel (Eds)., 2000. Springer Verlag, Lecture Notes in Computer Science 1917 © Springer-Verlag.

• Watson, R.A. and Pollack, J.B. (2001). Symbiotic Composition and Evolvability.
  Advances in Artificial Life, 6th European Conference, (ECAL 2001) , Prague, Czech Republic, September 10-14, 2001, Proceedings. Jozef Kelemen, Petr Sosik (Eds.): Lecture Notes in Computer Science 2159 Springer 2001, ISBN 3-540-42567-5. pp. 480-490.

• Spector, L., Harrington, K. and Helmuth, T. (2012). Tag-based Modularity in Tree-based Genetic Programming.
  Proceedings of the Genetic and Evolutionary Computation Conference, pp.815-822.

• Spector, L., Martin, B., Harrington, K. and Helmuth, T. (2011). Tag-Based Modules in Genetic Programming.
  Proceedings of the Genetic and Evolutionary Computation Conference, pp.

• Hornby, Gregory. S. and Pollack, Jordan. B. (2001). The Advantages of Generative Grammatical Encodings for Physical Design.
  Congress on Evolutionary Computation.

• Lowell, J. H. and Pollack, J. B (2014). The Effect of Connection Cost on Modularity in Evolved Neural Networks.
  Proceedings of Artificial Life 2014.

• Ozisik, A. P. and Harrington, K. I. (2012). The Effect of Tags on the Evolution of Honest Signaling.
  Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp.345-352.

• Lowell, J. H. and Pollack, J. B. (2017). The effects of environmental structure on the evolution of modularity in a pattern classifier.
  Proceedings of European Conference on Artificial Life, Lyon France, 267-274.

• Harrington, K. I., Ozisik, A. P. and Pollack, J. (2012). The Effects of Finite Populations and Selection on the Emergence of Signaling.
  Proceedings of Artificial Life XIII, pp.194-201.

• Rieffel, J. and Pollack, J. (2004). The Emergence of Ontogenic Scaffolding in a Stochastic Development Environment.
  Proceedings of the 2004 Genetic and Evolutionary Computation Conference, Springer Verlag, 2004.

• Saunders, G. M. and Pollack, J. B. (1996). The Evolution of communication schemes of continuous channels.
  Proceedings 4th International conference on Simulation of Adaptive Behavior.

• Saunders, G. M. and Pollack, J. B. (1994). The Evolution of communication schemes of continuous channels.
  Ohio State University LAIR Tech report 94-GS-EVCOMM.

• Blair, Alan D. and Sklar, Elizabeth (1998). The evolution of subtle manoeuvres in simulated hockey.
  Proceedings of the Fifth International Conference of the Society for Adaptive Behavior. Pfeifer, Blumberg, Kobayashi, eds. Cambridge: MIT Press, 1998.

• Saunders, G., Kolen, J. F. and Pollack, J. B (1994). The Importance of Leaky Levels for behavior based AI.
  Proceedings of Third Int'l conference on Simulation of Adaptive Behavior.

• Pollack, J.B. (1991). The Induction of Dynamical Recognizers.
  Machine Learning 7, 227-252.

• Funes, P., Sklar, E., Juillé, H. and Pollack, J. (1997). The Internet as a Virtual Ecology: Coevolutionary Arms Races Between Human and Artificial Populations.
  Brandeis University Computer Science Technical Report CS-97-197.

• Kolen, J. F. and Pollack, J. B. (1995). The Observer's Paradox: Apparent computational complexity in physical systems.
  J Experimental and Theoretical AI, 7, 253-277.

• Hornby, G.S. (1996). The Recombination Operator, its Correlation to the Fitness Landscape and Search Performance.
  Master's Thesis, University of Alberta, Department of Computing Science, 1996.

• Lowell, J. H., Harrington, K. I. and Pollack, J. B. (2014). The Resilience of a Swarm Ecosystem Under Environmental Variation.
  Proceedings of Artificial Life 2014.

• Viswanathan, S. (2007). The secondary substrate problem in Co-Evolution and Developmental-Evolution.
  PhD Dissertation, Brandeis University, May 2007 .

• Bader-Natal, A. (2008). The Teacher's Dilemma: A game-based approach for motivating appropriate challenge among peers.
  PhD dissertation, Michtom School of Computer Science, Brandeis University.

• Burjorjee, Keki and Pollack, Jordan (2005). Theme Preservation and the Evolution of Representation .
  Theory of Representation Workshop at the the 2005 Genetic and Evolutionary Computation Conference .

• Burjorjee, Keki and Pollack, Jordan (2005). Theme Preservation and the Evolution of Representation .
  2nd Indian International Conference on Artificial Intelligence (IICAI-05) .

• Melnik, O. and Pollack, J.B. (2002). Theory and scope of exact representation extraction from feed-forward networks.
  Cognitive Systems Research 3(2), previously Brandeis CS Tech Report CS-99-205, see also a Results Web Page.

• Bucci, A. and Pollack, J.B. (2007). Thoughts on Solution Concepts.
  Proceedings of the 2007 Genetic and Evolutionary Computation Conference. Dirk Thierens et al. (editors). ACM Press, New York, NY.

• Pollack, Jordan. B., Lipson, Hod, Hornby, Gregory S. and Funes, Pablo (2001). Three Generations of Automatically Designed Robots.
  Artificial Life, 7:3.

• Sklar, E. and Pollack, J.B. (1998). Toward a Community of Evolving Learners.
  Third International Conference on the Learning Sciences (ICLS-98).

• Viswanathan, S. and Pollack, J. (2004). Towards an evolutionary-developmental approach for real-world substrates.
  in Pollack et al., Proceedings of the ninth international conference on the simulation and synthesis of living systems (Artificial Life IX), Boston, USA (September 2004), pp. 45 - 50, MIT Press, 2004 .

• Bader-Natal, A. and Pollack, J. (2005). Towards Metrics and Visualizations Sensitive to Coevolutionary Failures.
  AAAI Technical Report FS-05-03 Coevolutionary and Coadaptive Systems, AAAI Fall Symposium 2005, Washington D.C. .

• Sklar, Elizabeth, Blair, Alan D. , Funes, Pablo and Pollack, Jordan (1999). Training Intelligent Agents using Human Internet Data .
  IAT-99. (to appear).

• Melnik, O. and Pollack, J.B. (2000). Using Graphs to Analyze High-Dimensional Classifiers .
  IJCNN 2000, IEEE press, Presented at Montreal Workshop on Selecting and Combining Models with Machine Learning Algorithms .

• De Jong, Edwin D. and Pollack, Jordan B. (2001). Utilizing Bias to Evolve Recurrent Neural Networks.
  Proceedings of IJCNN 2001.

• Spector, L., Harrington, K., Martin, B. and Helmuth, T. (2011). What's in an Evolved Name? The Evolution of Modularity via Tag-Based Reference.
  Genetic Programming Theory and Practice IX, pp.1-16.

• Harrington, K. and Pollack, J. (2011). Zipper-based Meta-Genetic Programming.
  Technical Report Brandeis University CS-11-275.