Alife Seminar: Seminar summary in brief:

After a brief introduction to alife and genetic algorithms, we have focused on the computational evolution of autonomous agents. More specifically, we concentrated on neural-network guided agents, and mainly on such software robots (``softbots''). Our aim was to get a feeling of what can be currently achieved with such agents, that is, what tasks can they perform and what it takes to successfully evolve them. Issues addressed included encoding schemes, the interaction of learning and evolution, and `higher-level' functions. Finally, several other `applications' of alife to computer science (other than autonomous agents) were discussed.

Program

Summaries of the Talks given

  1. Genetic algorithms and Artificial life
  2. An evolutionary approach to synthetic biology
  3. Computer viruses - a from of artificial life?
  4. Modeling adaptive autonomous agents
  5. Evolution of homing navigation in a real mobile robot
  6. Incremental evolution of complex general behavior
  7. Using Emergent Modularity to Develop Control Systems for Mobile Robots
  8. The influence of learning on evolution
  9. Relearning and Evolution in Neural Networks
  10. Designing neural networks using genetic algorithms with graph generation system
  11. Evolving artificial neural networks that develop in time
  12. Toward synthesizing artificial neural networks that exhibit cooperative intelligent behavior
  13. Co-evolving high-level representations
  14. Evolution of communication in artificial organisms
  15. A Self-Organizing Spatial Vocabulary
  16. Educational and therapeutic Alife games
  17. Measurement of evolutionary activity, teleology, and life
  18. A case of Lamarckian Evolution
  19. Empirical Investigation of the Benefits of Partial Lamarckianism
  20. Polyworld: Life in a new context
  21. Evolving Visual Routines
  22. A Simple Model of Neurogenesis and Cell Differentiation
  23. Ant colony system: A cooperative learning approach to the traveling salesman problem
  24. Cooperation and Community Structure in Artificial Ecosystems
  25. Computer Immunology
  26. Modeling Simple Genetic Algorithms
  27. Genetic Algorithms as Global Random Search Methods
  28. Multi-Parent Reproduction in Genetic Algorithms