PostgreSQL
60.2. Genetic Algorithms
The genetic algorithm (GA) is a heuristic optimization method which operates through randomized search. The set of possible solutions for the optimization problem is considered as a population of individuals. The degree of adaptation of an individual to its environment is specified by its fitness.
The coordinates of an individual in the search space are represented by chromosomes, in essence a set of character strings. A gene is a subsection of a chromosome which encodes the value of a single parameter being optimized. Typical encodings for a gene could be binary or integer.
Through simulation of the evolutionary operations recombination, mutation, and selection new generations of search points are found that show a higher average fitness than their ancestors. Figure 60.1 illustrates these steps.
Figure 60.1. Structure of a Genetic Algorithm
+
According to the comp.ai.genetic FAQ it cannot be stressed too strongly that a GA is not a pure random search for a solution to a problem. A GA uses stochastic processes, but the result is distinctly non-random (better than random).
| Prev | Up | Next |
|---|---|---|
60.1. Query Handling as a Complex Optimization Problem |
60.3. Genetic Query Optimization (GEQO) in PostgreSQL |
Submit correction
If you see anything in the documentation that is not correct, does not match your experience with the particular feature or requires further clarification, please use this form to report a documentation issue.
Copyright © 1996-2023 The PostgreSQL Global Development Group