The best Artificial Intelligence results involve neural nets,
computing modeled on the architcture of the brain. |
Pursuing the biological paradigm further, John Holland
developed genetic algorithms, computer programs that can be used to solve problems by evolution. |
The basic genetic algorithm has seven steps. (Classifier system, fitness,
crossover, and mutation will be described soon.) |   1. Express solutions as a sequence of yes-no questions (a
classifier system for the problem). |   2. Start with a random initial distribution of proposed solutions, sequences of
answers to the yes-no questions. |   3. Try each proposed solution on test case problems. |   4. The fitness of each proposed solution is
how well it performs on the test cases. |   5. Remove the least-fit members of the population. |   6. Reward the most-fit by letting them reproduce by
crossover, with occasional mutation. |   7. Repeat steps 3-6 on the new population. |
First, a classifier system is a
way to encode possible solutions in a fashion similar to the storage architecture of genetic information. |
Next, the fitness represents the success of a proposed
solution with the test problems. Often this is thought of, at least metaphorically, as a landscape. |
The first genetic operation we use is crossover. |
In addition, we use mutation. |
Here is an application of genetic algorithms to a
problem involving cellular automata. |
Genetic algorithms have been used to solve a variety of design and optimization
problems. In addition to the walking robots mentioned earlier,
here are two interesting recent variations. |
Genetic algorithms applied to circuit design
Can a few thousand generations of machine evolution discover design possibilities undetected by a few
thousand electrical engineers? |
Genetic algorithms applied to art
Evolution, but is it art?. |
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