genetic algorithm

1.Introduction
Charles Darwin stated the theory of natural evolution in the
origin of species. Over several generations, biological organisms
evolve based on the principle of natural selection “survival of the
fittest” to reach certain remarkable tasks.
In nature, an individual in population competes with each other for
virtual resources like food, shelter and so on. Also in the same
species, individuals compete to attract mates for reproduction.
Due to this selection, poorly performing individuals have less
chance to survive, and the most adapted or “fit” individuals
produce a relatively large number of offspring’s. It can also be
noted that during reproduction, a recombination of the good
characteristics of each ancestor can produce “best fit” offspring
whose fitness is greater than that of a parent. After a few
generations, species evolve spontaneously to become more and
more adapted to their environment.
In 1975, Holland developed this idea in his book “Adaptation in
natural and artificial systems”. He described how to apply the
principles of natural evolution to optimization problems and built
the first Genetic Algorithms. Holland’s theory has been further
developed and now Genetic Algorithms (GAs) stand up as a
powerful tool for solving search and optimization problems.
Genetic algorithms are based on the principle of genetics and
evolution.
The power of mathematics lies in technology transfer: there exist
certain models and methods, which describe many different
phenomena and solve wide variety of problems. GAs are an
example of mathematical technology transfer: by simulating
evolution one can solve optimization problems from a variety of
sources. Today, GAs are used to resolve complicated optimization
problems, like, timetabling, job shop scheduling, games playing.
2. A Simple Genetic Algorithm
An algorithm is a series of steps for solving a problem. A genetic
algorithm is a problem solving method that uses genetics as its