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Next: Initial Experiments Up: Evolving Brick structures Previous: Mutation and Crossover

Evolutionary Algorithm

We use a plain steady-state genetic algorithm, initialized with a population of one single brick. Through mutation and crossover, a population of 1000 individuals is generated and then evolved:

While maximum fitness < Target fitness

Randomly select mutation or crossover.
Select 1 (for mutation) or 2 (for crossover) random individual(s) with fitness proportional probability.
Apply mutation or crossover operator
Generate physical model and test for gravitational load
If the new model will support its own weight

Then replace a random individual with it (chosen with inverse fitness proportional probability).

Pablo Funes