| Traditional software engineering dictates the use of modular and structured programming, and top-down stepwise refinement techniques, which reduce the amount of variability arising in the development process by establishing standard procedures to be followed while writing software. This focusing leads to a reduced variability in the resulting products, due to the use of standardized constructs.; Genetic Programming (GP) performs heuristic search in the space of programs. Programs produced through the GP paradigm emerge as the result of simulated evolution and are built through a bottom-up process, incrementally augmenting their functionality until a satisfactory level of performance is reached. Can we automatically extract knowledge from the GP programming process, that can be useful to focus the search and reduce product variability thus leading to a more effective use of the available resources?; An answer to this question is investigated with the aid of the Cultural Algorithm paradigm. A new system was developed in this work called Cultural Algorithms with Genetic Programming. CAGP embeds a genetic programming system within a cultural algorithm framework. The pool of genetic programs is the population level of CAGP. The microevolution within the population brings about potentially meaningful characteristics for the achievement of the solution, such as properties exhibited by the best performers in the population. CAGP generalizes upon these features and represents them as the set of the current beliefs. Beliefs correspond to constraints which all of the genetic operators and programs must follow. Interaction between the two levels occurs in one direction through the generalization process, and, in the other, through the modulation of an individual's program parameters according to which and how many of the constraints are followed.; CAGP is applied to solve an instance of the symbolic regression problem, in which a function of one variable needs to be discovered. The results of the experiments show an overall improvement on the average performance of CAGP over GP alone and a significant reduction of the complexity of the produced solution. Moreover, the execution time required by CAGP is comparable with the time required by GP alone. |
