1. Genetic Algorithms
GAs are one of the most powerful and applicable search methods available
GA originally developed by John Holland (1975)
Inspired by natural genetics and biological evolution
Uses concept of “survival of fittest” (fitness function)
Genetic operators (crossover, mutation, etc.) used to modify a pool of state candidates in order to improve them
Survival of the fittest, with reproduction of new possible individuals coming from best discovered parents
Iterative procedure (iterative improvement)
Produces a series of populations one per iteration
Each member of a population represents a feasible solution, called a chromosome

2. GA Pseudo Code

4. Genetic Operators

5. Selection Mechanisms Should be based on fitness, of course
Fitness proportionate selection
Tournament Selection:
Pick h1, h2 randomly
With probability p (p>0.5) select the more fit
Rank Selection:
Sort all hypothesis by fitness
Prob of selection is proportional to rank
Elitist Selection: Insure that at least 1 copy of the best individual survives

6. Specific vs General approaches
The genes of the individuals -- the genotype -- are used to determine how it behaves (i.e., how well it solves the problem) -- the phenotype.
The genetic operators manipulate the genes, thus they must be tied to the representation of the genes.
Genetic operators that are specific to the problem domain.
Significant research has been done, attempting to determine universal genetic operators, based on universal gene representations.
Unfortunately, these attempts have not been successful and it has been shown that problem specific encodings typically out perform
universal encodings

7. GA Pros/Cons Various Data Representations, One Algorithm
No fancy math involved in the algorithm, however designing an objective can be difficult and confusing
Easy to understand
Works on almost anything – must have objective function
Inherently parallel
Doesn’t work as well as other algorithms in convex (or mostly convex) search spaces – i.e. if you know a smart way to search the space, do it
Depending on complexity, a GA can be computationally expensive
Often requires a lot of tweaking

8. GA applet Init: 250 plants, 25 plant eaters
Plants tend to grow in clumps
If an eater bumps into a plant, it eats it
The more plants an eater eats, the better
Each iteration, a new generation of eaters is produced
New population produced through mutation and xover
Eater can see single square just in front of I
Can see: plant, empty space, eater, wall.
Eater has an internal (16 possible) state
At each time step eater can: move forward, move backwards, turn left, turn right. It can also change its internal state.
Decision on action based on internal state and what it sees in front of it (requires 64 rules).

9. More GA stuff What do GAs work on?: GA-easy vs GA-hard
The royal road
Determining basins of attraction