Applications of Genetic Algorithm

The Key of the success in Genetic Algorithm applications are effective GA representation and meaningful fitness evaluation. The demand of Genetic Algorithm comes from their grace and simplicity as robust search algorithms. Also from their power to discover good solutions quickly for complex high – dimensional problems. The benefit of using the GA approx is the ease to handle arbitrary kind of constraints and objectives. For problem –solving and modeling Gas have been used. Gas are applied to number of engineering and scientific problems, in business and entertainment including the following-

  • Automatic Programming: These algorithms are used to evolve computer programs for unique tasks and to design other computational structures. For instance storing Networks and cellular automata.
  • Optimization: These algorithms used in various optimization tasks which include numerical optimization and combinatorial optimization problem like circuit design. Job shop scheduling (JSP) and traveling salesman problem.
  • Models of social Systems: These algorithm are used to study evolutionary aspects of the social systems  like the evolution of communication trail-following behavior in ants and the evolution of cooperation
  • Ecological Models: To model ecological phenomenon like host parasite co-evolutions, biological arm races, symbiosis and resource flow in ecologies Gas are used.
  • Economic Models: To model process of innovation, the development of bidding strategies and the emergence of economic markets Gas are used.
  • Population Genetics Models: GA are employed to study question in population genetics, like ‘under water situations will a gene for recombination be evolutionary viable?
  • Immune System Models: To Model various aspects of the natural immune system which include somatic mutation during an individual’s life time and discovery of multi-gene families during evolutionary time Gas are used.
  • Machine and Robot Learning: Genetic algorithm are used to control and design robots, symbolic production systems, to evolve rules for learning classifier and to design neural networks. These algorithms are also used machine learning applications that include prediction and classification.
  • Interaction between Evolution and Learning: To study how individual learning and species evolution affect one another Gas are used.