Chapter 9. Genetic Algorithms
In most programming tasks, we humans carefully state the problem, code an algorithm to solve the problem, and turn the computer loose to run the algorithm. Then we hope the correct solution pops out, and if it doesn’t we send an irate bug report to the people who developed our programming language.
Genetic algorithms (GAs) offer a different approach: we still carefully state the problem, but we let the computer find a suitable algorithm and apply that algorithm to the problem to produce a solution. Generating algorithms programmatically usually means working with code as data, which has traditionally left it more in the realm of LISP and related languages. But since Perl can generate and evaluate code on the fly, it’s capable of handling generalized GAs.
The core principle behind a GA is that of evolution. You start with a set of random organisms, each of which is a program. You then run each of these programs and determine their fitness, which is the degree to which they succeed at the required task. Once the fitness of each is determined, you jump through some hoops to remove bad entries (natural selection), randomly permute some remaining entries (mutation), and intermingle other entries (hot algorithmic sex).
After repeating this process through several generations, the population will begin to converge on a solution. Often, it is not quite the solution you were expecting, and therein lies a lot of the fun in building a GA engine. ...
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