The use of genetic algorithms to solve computer science problems is an application of gene theory. Each basic element is represented as a gene. A combination of these basic elements is used to represent a solution, called a chromosome. A set of such chromosomes is taken together to form a population, representing a set of solutions to start with. Biological operations are then performed on these solutions to obtain other solutions. Each newly obtained solution is tested for fitness using a fitness criterion to check if the new solution is better than the already-known solutions. In this way, new solutions are obtained for nonpolynomial time problems in polynomial time. However, there is no guarantee that the solution obtained will be the best possible one.

The authors attempt to describe how a genetic algorithm approach can work for network topology design. The basic concepts are described very clearly and concisely. However, the advanced topics that would improve network topology design are left untouched.

The approach of this paper is very good, showing all the basic steps one needs to follow to apply genetic algorithms to network topology design. The paper also clearly brings out the concepts, by describing them using practical examples. However, complex concepts like fitness sharing and speciation are not covered. Inclusion of these concepts might have made the paper more interesting.