Bio-inspired electronic circuits have the potential to address some of the shortcomings of conventional electronic circuits, such as lack of applicability to ill-defined problems, of robustness, or of adaptivity to unexpectedly changing environments.
Bio-inspired circuits are designed by taking inspiration from principles observed in biology. The evolution of biological organisms, their development from a fertilized egg, and their learning capabilities are three sources of bio-inspiration that can be used for this purpose.
Until now bio-inspired electronics mostly focused on a single aspect of bio-inspiration: either evolution, development or learning. In this thesis we consider that electronic circuits should encompass all three aspects to fully benefit from bio-inspiration. These circuits capable of evolution, development and learning are called POEtic circuits (POE stands for phylogeny, ontogeny and epigenesis, that mean respectively evolution, development and learning).
Conceptually these POEtic circuits, much like biological organisms, are multi-cellular circuits that evolve following the principles of selection and differential reproduction, they develop from a single cell and differentiate according to inter-cellular and environmental signals, and eventually they learn during their lifetime. These circuits may also dynamically reorganize their structure in order to cope with changes in the environment, or when they are expanded with new cells, sensors or actuators. In comparison to conventional circuits, POEtic circuits are created automatically using evolutionary principles, even if only a partial or high-level specification of the problem is known. Development provides a complex genotype to phenotype mapping, that may lead to fault-tolerance or adaptive development in order to cope with environmental changes. Finally learning allows these circuits to memorize past events or adapt their response over time to improve their behavior.
This thesis deals with the evolutionary mechanisms required to evolve these POEtic circuits. We argue that in order to fully realize the potential of POEtic circuits a novel evolutionary system that takes into account their characteristics and that encompasses both a genetic encoding and a developmental system is required. Indeed, evolutionary algorithms commonly used to evolve electronic circuits do not exploit the complex dynamics of development which is seen in biological organisms. They generally use a direct genetic encoding with a one to one genotype to phenotype mapping. As a consequence the genetic string grows with the size of the circuits and this may limit the scalability of the evolutionary approach to larger circuits. Furthermore these encodings do not allow intercellular or environmental interactions during development, which could lead to adaptive development or fault-tolerant circuits.
In this thesis we develop an evolutionary system suited for multi-cellular POEtic circuits. This evolutionary system is inspired by the mechanisms of gene expression and cellular differentiation seen in biological organisms. It attempts to provide better evolvability and scalability than direct genetic encodings, it allows cellular or environmental interactions during development, and it is computationally simple so that it can be efficiently implemented in hardware. It is furthermore generic, and it makes minimal assumptions on the circuits that are evolved: other than assuming they are multi-cellular, it only requires local communication between neighboring cells.
We demonstrate the proposed evolutionary system by evolving multi-cellular circuits for a wide range of applications. The results that we obtain confirm the generality of our approach and its advantages in comparison to direct genetic encodings.
The proposed evolutionary system is used to evolve structures of differentiated cells, and it shows better scalability to larger structures in terms of fitness than a direct genetic encoding. The dynamics of development within the evolutionary system can recover these structures in case of faults, even at high fault rates. The proposed evolutionary system is used to evolve multi-cellular circuits composed of spiking neurons to recognize patterns and to control the navigation with obstacle avoidance of a mobile robot, and in comparison it outperforms a direct genetic encoding. Finally it is used to evolve circuits capable of learning that control a mobile robot in a vision-based learning and navigation task. This last application demonstrates the three aspects of bio-inspiration of POEtic circuits in a single task: evolution, development and learning.