Mechanism of GA

A GA is an algorithm that operates on a principle, a combination of natural selection and procreation permits the development of living species that are highly adapted to their environments.

When applied to a problem the standard genetic algorithm proceeds as follows: an initial population of individuals i.e., a set of solutions for the given problem represented by chromosomes, is generated at random or heuristically.

At every evolutionary step, known as a generation, the individuals in the current population are decoded and evaluated according to some predefined quality criterion, referred to as the fitness function.

To form the next generation, individuals are selected according to their fitness. Then some or the entire existing individual of the current population is replaced with the newly created individuals.

Creation of new individuals is done by crossover and mutation operations which motivated by a hope that the new population will be better than the old one. Many selection procedures are currently in use, one of the simplest being Holland's original fitness proportionate selection, where individuals are selected with a probability proportional to their relative fitness.

This ensures that the expected number of times an individual is chosen is approximately proportional to its relative performance in the population. Thus, high-fitness individuals stand a better chance of “reproducing”, while low-fitness ones are more likely to disappear.

This is repeated until some condition such as number of populations or improvement of the best solution is satisfied. If the GA has been designed well, the population will converge to an optimal solution to the problem.

Convergence is the progression towards increasing uniformity. However, it is important not to forget that GA are stochastic iterative processes and they are not guaranteed to converge; hence, the termination condition may be specified as some fixed, maximal number of generations or as the attainment of an acceptable fitness level.^†

References

†“Genetic Algorithms”, Burhaneddin SANDIKCI, http://www.ie.bilkent.edu.tr/~lors/ie572/burhaneddin_html/IE572_GA.html

http://www.ie.bilkent.edu.tr/~lors/ie572/burhaneddin.pdf

Related Posts:

• ABSTRACT
• INTRODUCTION
• JOB SHOP SCHEDULING
• Search Methods and Optimization Techniques
• Some other Optimisation methods
• Genetic Algorithm
• Darwin's Theory of Natural Selection

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© 2006 Kumaravel & Project Team

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