Transgenic, an operator for evolutionary algorithms

In the 1950s and the 1960s several computer scientists independently studied evolutionary systems with the idea that evolution could be used as an optimization tool for engineering problems. For these evolutionary-computation researchers, the mechanisms of evolution seem well suited for some of the most pressing computational problems in many fields. Ideas from Genetics are usually incorporated into evolutionary algorithms, such as: haploid crossover, mutation, diploid, inversion, gene doubling, deletion, and others. In the present study, we proposed an operator, named transgenic, for evolutionary algorithms, especially designed for Genetic Algorithms (GA). This operator is inspired by genetically modified organisms (GMOs), where important features are introduced into their genome artificially. The transgenic operator uses historical information to choose the best attributes, converging to better results faster than traditional GAs. The GA, used in this study, allows the discovery of concise, yet accurate, high-level rules (from a biological and synthetic database) which can be used as a classification system. The obtained results show that transgenic operator is promising at obtaining better of the the same results with a lower number of generations and smaller populations.