minus one [1, L-l]. Swapping all characters between i+1 and L forms two new strings.

For example:

 S1 = 1 1 0 11 Note: The I character
 indicates the crossover
 S2 = 1 0 0 0 1 site.

i is chosen at random to be 3. After crossover two new strings are formed which are part

of the new generation. The new strings are as follows:

 Si'= 11001

 S2' = 10 0 1 1

Mutation

 Mutation is necessary because, even though reproduction and crossover effectively

search and recombine, there are occasions when there can be a loss of genetic material.

In addition, mutation adds extra variability to the existing population. "Mutation alone

does not generally advance the search for a solution, but it does provide insurance against

the development of a uniform population incapable of further evolution" [7, p. 3].

 In the simple genetic algorithm, mutation usually has a very small probability and

simply corresponds to a random alteration of the value of a string position. Because

mutation rates are low, it can be considered a secondary mechanism of genetic algorithm

adaptation [6].

 Genetic Algorithm Implementation

 A significant portion of this research was devoted to developing a method to measure

how well the current robot configuration covered the region to minimize the shadow area

while making sure that every communications repeater robot was within line-of-sight of

another. In order to gauge the results obtained from the genetic algorithm it was

necessary to develop an efficient and robust objective function. In particular, the