Week 7 introduced Genetic Algorithms, who’s effectiveness is somewhat disputed. In any case, these algorithms are quite interesting in their balance between a kind of hill climbing (fitness function) and stochastic methods (cross over, mutation).

The lecture gave the natural basis for these algorithms and defined the key components:

  • Chromosome (ie 101101101)
  • Reproduction (ie crossover/roulette/tournament)
  • Mutation
  • Fitness functions

Genetic Algorithms can find good solutions in large search spaces quickly

The second half of the lecture was dedicated to assignment and unit test revision.