Genetic Algorithms search by simulating evolution, starting
from an initial set of solutions or hypotheses, and generating
successive "generations" of solutions.
This particular branch of
AI was inspired by the way living things evolved into more
successful organisms in nature. The main idea is survival
of the fittest, a.k.a. natural selection.
A chromosome is a long, complicated thread of DNA (deoxyribonucleic
acid). Hereditary factors that determine particular traits of an
individual are strung along the length of these chromosomes, like
beads on a necklace. Each trait is coded by some combination of
DNA (there are four bases, A (Adenine), C (Cytosine), T (Thymine)
and G (Guanine). Like an alphabet in a language, meaningful combinations
of the bases produce specific instructions to the cell.
Changes occur during reproduction. The chromosomes from the
parents exchange randomly by a process called crossover.
Therefore, the offspring exhibit some traits of the father and
some traits of the mother.
A rarer process called mutation also changes some traits.
Sometimes an error may occur during copying of chromosomes
(mitosis). The parent cell may have -A-C-G-C-T- but an accident
may occur and changes the new cell to -A-C-T-C-T-. Much like a
typist copying a book, sometimes a few mistakes are made. Usually
this results in a nonsensical word and the cell does not survive.
But over millions of years, sometimes the accidental mistake
produces a more beautiful phrase for the book, thus producing a
better species.