  Peptide Optimization is a highly complex problem and it takes very long time
of computation. This optimization process uses many software applications in a
cluster running GNU/Linux Operating System that perform special tasks. The
application to organize the whole optimization process had been already
developed, namely SEPP (System for Evolutionary Pareto Optimization of
Peptides/Polymers). A single peptide optimization takes a lot of computation
time to produce a certain number of individuals. However, it can be accelerated
by increasing the degree of parallelism as well as the number of nodes
(processors) in the cluster. In this master thesis, I build a model simulating
the interplay of the programs so that the usage of each resource (processor)
can be determined and also the approximated time needed for the overall
optimization process. There are two Evolutionary Algorithms that could be used
in the optimization, namely Generation-based and Steady-state Evolutionary
Algorithm. The results of each Evolutionary Algorithm are shown based on the
simulations. Moreover, the results are also compared by using different
parameters (the degree of parallelism and the number of processors) in the
simulation to give an overview of the advantages and the disadvantages of the
algorithms in terms of computation time and resource usage. The model is built
up using JavaSpaces Technology.
