Introduction to Genetics Algorithms

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.