4 papers
Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics
K. Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen
The co-optimization of a robot's body and brain presents a coupled challenge: the morphology constrains which control strategies are effective, while the control determines how wel…
Integrating Sample Inheritance into Bayesian Optimization for Evolutionary Robotics
K. Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen
In evolutionary robotics, robot morphologies are designed automatically using evolutionary algorithms. This creates a body-brain optimization problem, where both morphology and con…
Generational Replacement and Learning for High-Performing and Diverse Populations in Evolvable Robots
K. Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen
Evolutionary Robotics offers the possibility to design robots to solve a specific task automatically by optimizing their morphology and control together. However, this co-optimizat…
More complex environments may be required to discover benefits of lifetime learning in evolving robots
Ege de Bruin, Kyrre Glette, Kai Olav Ellefsen
It is well known that intra-life learning, defined as an additional controller optimization loop, is beneficial for evolving robot morphologies for locomotion. In this work, we inv…