Adaptively Evolving Probabilities of Genetic Operators

This work is concerned with proposing an adaptive method to dynamically adjust genetic operator probabilities throughout the evolutionary process. The proposed method relies on the individual preferences of each chromosome, rather than the global behavior of the whole population. Hence, each individual carries its own set of parameters, including the probabilities of the genetic operators. The carried parameters undergo the same evolutionary process as the carriers--the chromosomes - do. We call this method Evolved Evolutionary Algorithm (E2A) as it has an additional evolutionary process to evolve control parameters. Furthermore, E2A employs a supplementary mutation operator (DE-mutation) which utilizes the previously overlooked numerical optimization model known as the Differential Evolution to expedite the optimization rate of the genetic parameters. To leverage our previous work, we used Gene Expression Programming (GEP) as a benchmark to determine the performance of our proposed method. Nevertheless, E2A can be easily extended to other genetic programming variants. As the experimental results on a wide array of regression problems demonstrate, the E2A method reveals a faster rate of convergence and provides fitter ultimate solutions. However, to further expose the power of the E2A method, we compared it to related methods using self-adaptation previously applied to Genetic Algorithms. Our benchmarking on the same set of regression problems proves the supremacy of our proposed method both in the accuracy and simplicity of the final solutions.