On the Parameterization of Cartesian Genetic Programming
In this work, we present a detailed analysis of Cartesian Genetic Programming (CGP) parametrization of the selection scheme ($\mu+\lambda$), and the levels back parameter l. We also investigate CGP’s mutation operator by decomposing it into a self-recombination, node function mutation, and inactive gene randomization operators. We perform experiments in the Boolean and symbolic regression domains with which we contribute to the knowledge about efficient parametrization of two essential parameters of CGP and the mutation operator.
A study on graph representations for genetic programming
Graph representations promise several desirable properties for Genetic Programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behavior of Cartesian Genetic Programming (CGP), Linear Genetic Programming (LGP), Evolving Graphs by Graph Programming (EGGP) and traditional GP. By fixing some aspects of the config…
COVID-19: A Survey on Public Medical Imaging Data Resources
This regularly updated survey provides an overview of public resources that offer medical images and metadata of COVID-19 cases. The purpose of this survey is to simplify the access to open COVID-19 image data resources for all scientists currently working on the coronavirus crisis.