Behavior finding: Morphogenetic Designs Shaped by Function

Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism’s morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design “organic” structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding

Evolutionary Development based on Genetic Regulatory Models for Behavior-Finding

This thesis investigates the role, implications, and applications of a genetic-regulated developmental process in evolution. A novel formalism of string-regulated graph grammar, as an abstraction of biological development, is presented and studied. Founded on this formalism, evolutionary developmental models are introduced and tested. The results demonstrate the benefits of the models, compared to traditional direct encoding approaches, for the problems of form-finding and behavior-finding

The Evolution of Complexity in Autonomous Robots

Evolutionary robotics–the use of evolutionary algorithms to automate the production of autonomous robots–has been an active area of research for two decades. However, previous work in this domain has been limited by the simplicity of the evolved robots and the task environments within which they are able to succeed. This dissertation aims to address these challenges by developing techniques for evolving more complex robots. Particular focus is given to methods which evolve not only the control policies of manually-designed robots, but instead evolve both the control policy and physical form of the robot. These techniques are presented along with their application to investigating previously unexplored relationships between the complexity of evolving robots and the task environments within which they evolve