Biologically Inspired Computing and Artificial Life

Computer Science core course, European Credit Transfer System: 5 ECTS credits.

Lecture summary

  1. Optimization
    1. Problems, instances and models
    2. Greedy and Steepest local optimization
    3. Simulated annealing
    4. Tabu Search
  2. Evolutionary algorithms
    1. Classification
    2. Genetic algorithms
      1. Parameters and evaluation
      2. Selection
      3. Crossover
      4. Mutation
      5. Other operators and techniques
      6. Scaling
      7. Summary of GA parameters
      8. Schema theorem
      9. Deceptive problems and the "No Free Lunch" theorem
      10. Nature-inspired techniques
      11. Messy genetic algorithm
      12. Empirical and theoretical evaluation of a GA
    3. Evolutionary strategies
    4. Differential evolution
    5. Evolutionary programming
      1. Floating point representation
      2. Embryogeny
      3. Crossover and global convexity
    6. Genetic programming
      1. Symbolic regression
    7. Classifier systems (CFS/GBML)
      1. Input and output interfaces
      2. Main cycle
      3. Learning Classifier System (LCS)
      4. The Bucket Brigade algorithm
      5. Rule discovery
      6. Summary
    8. Other techniques in EA
      1. Multiple objectives
      2. Incorporating knowledge
      3. Constraints
      4. Sample applications
    9. Parallel EA
    10. Summary of evolutionary optimization
    11. Co-evolutionary architectures
      1. Competitive
      2. Cooperative
    12. Bibliography and resources
  3. Other optimization techniques and computational environments
    1. Swarm intelligence and ant colony optimization (AA, ACO)
    2. Particle Swarm Optimization (PSO)
    3. Artificial Immune Systems (AIS)
    4. Molecular computing, DNA computation
    5. Quantum computing
    6. Membrane computing
  4. Artificial Life
    1. What is life, what is not: definitions of life
    2. Research interests
    3. Evolution
      1. Why evolution?
      2. Theories
      3. Discussion
      4. Evolution in Artificial Life
    4. Emergence in Boids
    5. Spatio-temporal dynamics in Cellular Automata
    6. Spontaneous (and open-ended) evolution in Tierra
    7. Directed (guided) evolution
      1. Karl Sims – virtual creatures
      2. Co-evolution of Pursuit and Evasion
      3. Evolutionary design
      4. Robotics: robot control with layers
      5. Building brains
      6. Building brains and bodies
      7. Sensor evolution
    8. Agent and environment
      1. Complex Adaptive Systems (CAS), Multi-Agent Systems (MAS)
      2. Embodiment and how to measure it
      3. Levels of autonomy
      4. Representation of concepts
    9. Formal description of evolving systems
    10. Elements of game theory and evolutionary games
      1. Basic terms and concepts
      2. Models
      3. Strategies of social behaviors and social dilemmas
    11. Models of biological life – selected examples
      1. Herrnstein's matching law
      2. Evolution of nervous systems
      3. Altruism and the Hamilton's rule
      4. Study of sexual signals and the handicap principle
      5. Analysis of social learning
      6. Dynamics of gene expression
      7. Heuristics in feeding offspring
      8. Evolution of communication and languages
      9. Biologically inspired collective robotics
    12. Frontiers of knowledge
    13. Bibliography and resources


Lab summary

The following simulation environments are presented in the lab (~1h each): sodarace, mcell/ddlab, starlogo/netlogo, framsticks, repast, einstein, and others. Selected topics are discussed (spiking neurons, Artificial Chemistries, genetic regulatory networks, biologically inspired robotics, Lotka-Volterra equations, neural morphogenesis, etc.).

Finally, students design and perform an artificial life experiment in software or hardware (individual assignments), and write a report.