The software cost estimation process is one of the most critical managerial activities related to project planning, resource allocation and control. As software development is a highly dynamic procedure, the difficulty of providing accurate cost estimations tends to increase with development complexity. The inherent problems of the estimation process stem from its dependence on several complex variables, whose values are often imprecise, unknown, or incomplete, and their interrelationships are not easy to comprehend. Current software cost estimation models do not inspire enough confidence and accuracy with their predictions. This is mainly due to the models' sensitivity to project data values, and this problem is amplified because of the vast variances found in historical project attribute data. This paper aspires to provide a framework for evolving value ranges for cost attributes and attaining mean effort values using the AI-oriented problem-solving approach of genetic algorithms, with a twofold aim. Firstly, to provide effort estimations by analogy to the projects classified in the evolved ranges and secondly, to identify any present correlations between effort and cost attributes.