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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
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Should be based on fitness, of course
Fitness proportionate selection
Prhi  
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Fitnesshi 
 Fitnessh j 
Tournament Selection:
– Pick h1, h2 randomly
– With probability p (p>0.5) select the more fit
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Rank Selection:
– Sort all hypothesis by fitness
– Prob of selection is proportional to rank
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Elitist Selection: Insure that at least 1 copy of the best individual survives
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
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
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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).
More GA stuff
• What do GAs work on?: GA-easy vs GA-hard
– The royal road
• Determining basins of attraction