Genetic Algorithms-are here to stay

"Everything that can be invented has already been invented" was the famous quote of a scientist named Albert Michaelson in 1894. It was quoted long before some of the powerful inventions of mankind such as atomic bomb,aeroplanes.

Mankind has therefore never been left to feel complascent of its achievements.Time and again a new discovery revolutionises the world and creates a completely new environment for the people to live in.

The mechanical problem solvers,later came to be known as computers, were thought to be a mere science fiction until john von neuman laid down his architecture for a practical computer.It was the turn of the Transistors, in the early 60s, to revolutionise the computing world.The screeching,gigantic mechanical devices were scaled down to 'compact' ones to fit into a room. contemporary inventions in the field of electronics have further helped to diminish their size.

The computers of today have shrunk the world into a single entity,a global village.Information exchange,Email,file sharing would have remained one of those Michael Chriton thingies but for the Internet. Evidently such complex services require complex algorithms that strive to reduce the computing time to a minimum.

The computing problems can be broadly divided into two categories -P(polynomial complexity) and the NP(Non-polynomial complexity). The former class has specific algorithms to solve them efficiently while the conventional algorithms suffer with the latter. One such NP problem is the Travelling Sales Man (TSP) problem which has a time complexity of O(2^n), ie for every addition of a city the complexity of the problem doubles.

It is to tackle such problems that we need a more intelligent approach. An approach that's totally different,since the conventional algorithms have accepted a sullen defeat. A revolutionary concept to combat problems stated above is the Genetic Algorithms(GA).

GA differs from its conventional counterpart in a lot of ways. The solution offered by the GA is unpredictable. Most of the times,however,it gifts its users with an incredibly good solution. Having made the reader come upto this, the author feels a sense of responsibility to bestow him/her with some of his (mis)interpretations of the concept.

A baby baring a few basic instincts,such as sucking the mother's milk(takes a lot of concentration to adhere to the topic),is born naive. It however learns as it grows older. Its intelligence therefore increases with time. GA adpots a similar approach.

The solutions produced in the first generation are quite unacceptable while the one's produced by the later generations are better.Generation 1 therefore consists only of a set of random solutions. One among this is chosen depending on how well it satisfies the fitness function.

Fitness function hence is responsible to select one out of the many samples in a generation. It embodies the concept of Charles Darwin's theory - "survival of the fittest". Once the fitness function is properly defined, the programmer can sit back to watch the program grow intelligent!

After a sample is picked by the above mentioned function, it is subjected to Cross-over and Mutation to give rise to the next generation which is more "intelligent" and adapted to its environment.It is evident that as the number of generations increases the solution obtained gets bettered.

The author feels confident of the immense potential of the GA, atleast until the project work is over, they have certainly come here to stay,more importantly revolutionise.