Evolving Motor Techniques for Artificial Life

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Evolving Motor Techniques for Artificial Life
Kelley Hecker, Period 7
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Evolving Creatures

• Creatures develop more advanced motor techniques
• Progression from random movements to
  sophisticated patterns
• Possibility of specialized creatures
• Co-evolution
• Both the body and the brain develop

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Creature Genomes

• Genome represented by a one-dimensional array
• Each array has several nodes
• Nodes represent body segments
• Each node contains dimensions for body part,
  location of parent and child connections, and
  neuron functions
• Genome converted to physical form for simulation

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Methodology Controller

• Controller object maintains an array of genomes
• Generates new genomes at beginning of simulation
• Displays creatures and runs simulation
• Measures fitness and breeds creatures for next
  generation

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Methodology Nodes

• Physical dimensions
• Where the body segment connects to its parent and
  children segments
• Neuron functions
• Methods to add children and connection points
• Accessor methods return children, dimensions and
  neurons

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Methodology Creature GA

• Applies neuron functions to sensor values
• Possible neuron functions
• Oscillating functions sin, cos, atan, saw-wave
• Other functions sum-threshold, sign-of, min,
  max, mem, log, expt, devide, interpolate,
  differentiate
• Returns joint velocity values for the associated
  body segment

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Circulation of Data
Values received from joint-angle sensors
Values become effectors and modify joint velocity
Values sent to the GA, where they are put through
node's neurons
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Reproduction

• Creatures are evaluated based on their
  performance in the simulation
• Top fifth of genomes copied directly (asexual)?
• Remaining 4/5 of genomes are either reproduced
  asexually, crossed over with another genome, or
  grafted with another genome

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Simulation Process

• Population is simulated in the physical
  environment
• After ¼ of the simulation is over, weakest
  creatures are removed
• Final fitnesses are evaluated and reproduced
• Repeated with next generation for n generations

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Testing

• Fitness tests
• Measures the success of a motor method
• Progression of fitness level shows evolution of
  technique
• Create graph of fitness level over time

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3rd Quarter

• Finish re-writing code
• Implement reproduction and fitness evaluation
• Implement mutation