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

GA Pseudo Code

Genetic Operators

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

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 Specific vs General approaches

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

GA applet Init: 250 plants, 25 plan 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).