Genetic Algorithms are search techniques for global optimization in a complex search space. One of the interesting features of a Genetic Algorithm is that they lend themselves very well for parallel and distributed processing. This feature of Genetic Algorithm is useful in improving its computation efficiency for complex optimization problems. In this paper, we have implemented Genetic Algorithm in a distributed environment such that its implementation problem independent. This key attribute of distributed implementation allows it to be used for different types of optimization problems. Fault tolerance and user transparency are two other important features of our distributed Genetic Algorithm implementation. The effectiveness and generality of Genetic Algorithms have been demonstrated by solving two problems of network topology design and file allocation.
['Alok Srivastava', 'Anup Kumar', 'Rakesh M. Pathak']
Distributed Approach for Implementing Genetic Algorithms