#computers programming

[ytn01]

Programming at the end of the day

[webprogrammers]

Genetic Algorithms

The usual method for getting a computer to perform a task is to specify the task clearly, choose the suitable approach, and then implement and test the code. However, this approach requires that the programmer first know the suitable approach, and even when there are many potentially appropriate algorithms, it isn’t always clear which will prove optimal.

Starting in the 1960s, however, researchers began to explore the idea that an evolutionary approach might be flexible to programming. Biologists today know that nature did not begin with a set of highly optimized algorithms. Rather, it addressed the problems of survival through a production of alternatives through mutation and recombination that are then subjected to natural selection, with the most successful organisms surviving to reproduce. Researchers began to develop computer programs that emulated this process. A genetic program consists of a number of copies of a routine that contain encoded genes that represent elements of algorithms. The routines are given a task, such as sorting data or recognizing patterns, and the most successful routines are allowed to reproduce by exchanging genetic material. The new generation is then allowed to tackle the problem, and the process is recurring. As a result, the routines become increasingly efficient at solving the given problem, just as organisms in nature become more faultlessly adapted to a given environment.