Genetic Algorithms

Melanie Mitchell provides an introduction to how genetic algorithms work:

Many computational problems require a computer program to be adaptive—to continue to perform well in a changing environment. This is typified by problems in robot control in which a robot has to perform a task in a variable environment, or computer interfaces that need to adapt to the idiosyncrasies of an individual user. Other problems require computers to be innovative—to construct something truly new and original, such as a new algorithm for accomplishing a computational task, or even a new scientific discovery. Finally, many computational problems require complex solutions that are difficult to program by hand. A striking example is the problem of creating artificial intelligence. Early on, AI practitioners believed that it would be straightforward to encode the rules that would confer intelligence in a program; expert systems are a good example. Nowadays, many AI researchers believe that the “rules” underlying intelligence are too complex for scientists to encode in a “top-down” fashion, and that the best route to artificial intelligence is through a “bottom-up” paradigm. In such a paradigm, human programmers encode simple rules, and complex behaviors such as intelligence emerge from these simple rules. Connectionism (i.e., the study of computer programs inspired by neural systems) is one example of this philosophy (Smolensky, 1988); evolutionary computation is another.

For fun and completely inappropriate implementations of genetic algorithms in T-SQL, William Talada and Gail Shaw have us covered.

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