Romera-Paredes and colleagues’ work is the latest step in a long line of research that attempts to create programs automatically by taking inspiration from biological evolution, a field called genetic ...

Cartesian Genetic Programming (CGP) is a robust evolutionary algorithm that represents computational solutions as directed graphs, incorporating redundant genes to enhance the efficiency of its search ...

Collaborative research defines a novel approach to understanding how certain proteins called transcription factors determine which genetic programs will drive cell growth and maturation. The method, ...

We use genetic programming to find variants of the well-known Nawaz, En-score and Ham (NEH) heuristic for the permutation flow shop problem. Each variant uses a different ranking function to ...

Abstract: Computational gaming requires the automatic generation of virtual opponents for different game levels. We have turned to artificial evolution to automatically generate such game players. In ...

Abstract: Genetic programming has been positioned as a fit-for-purpose approach for symbolic regression. Researchers tend to select algorithms that produce a model with low complexity and high ...

Using genetic programming techniques to find technical trading rules, we find strong evidence of economically significant out-of-sample excess returns to those rules for each of six exchange rates ...

We’re living in the age of Big Data. As the driving force behind everything from search algorithms to surgical robots and machine learning, massive sets of data can be found at the heart of some of ...

Researchers have developed a new technology that improves the precision and integration density of synthetic genetic circuits. Professor Jongmin Kim's research team at POSTECH developed a new ...