Using quality farms in multi-objective genetic software architecture synthesis

Genetic algorithms have become a popular heuristic technique to solve difficult search problems. However, in multi-objective problem solving, like software architecture generation, the basic variation mechanisms of genetic algorithms (mutation and crossover) tend to lead to mediocre solutions as the evolution favors balancing of several quality properties. In this paper, we explore the acceleration of genetic software architecture generation using a novel approach based on so-called quality farms, i.e., populations which favor a certain quality property. We hypothesize that by crossbreeding individuals from different quality farms it is possible to create beneficial variance that raises the fitness value to a significantly higher level. Experiments suggest that farm-based crossbreeding improves fitness value about 10%.