Scale invariance in natural and artificial collective systems : a review

Self-organized collective coordinated behaviour is an impressive phenomenon, observed in a variety of natural and artificial systems, in which coherent global structures or dynamics emerge from local interactions between individual parts. If the degree of collective integration of a system does not depend on size, its level of robustness and adaptivity is typically increased and we refer to it as scale-invariant. In this review, we first identify three main types of self-organized scale-invariant systems: scale-invariant spatial structures, scale-invariant topologies and scale-invariant dynamics. We then provide examples of scale invariance from different domains in science, describe their origins and main features and discuss potential challenges and approaches for designing and engineering artificial systems with scale-invariant properties

Gene Regulatory Network Evolution Through Augmenting Topologies

International audienceArtificial gene regulatory networks (GRNs) are biologically inspired dynamical systems used to control various kinds of agents, from the cells in developmental models to embodied robot swarms. Most recent work uses a genetic algorithm (GA) or an evolution strategy in order to optimize the network for a specific task. However, the empirical performances of these algorithms are unsatisfactory. This paper presents an algorithm that primarily exploits a network distance metric, which allows genetic similarity to be used for speciation and variation of GRNs. This algorithm, inspired by the successful neuroevolution of augmenting topologies algorithm's use in evolving neural networks and compositional pattern-producing networks, is based on a specific initialization method, a crossover operator based on gene alignment, and speciation based upon GRN structures. We demonstrate the effectiveness of this new algorithm by comparing our approach both to a standard GA and to evolutionary programming on four different experiments from three distinct problem domains, where the proposed algorithm excels on all experiments

A gene regulatory network model for control

The activity of a biological cell is regulated by interactions between genes and proteins. In artificial intelligence, this has led to the creation of developmental gene regulatory network (GRN) models which aim to exploit these mechanisms to algorithmically build complex designs. The emerging field of GRNs for control aims to instead exploit these natural mechanisms and this ability to encode a large variety of behaviours within a single evolvable genetic program for the solution of control problems. This work aims to extend the application domain of GRN models to previously unsolved control problems; the focus will here be on reinforcement learning problems, in which the dynamics of the system controlled are kept from the controller and only sparse feedback is given to it. This category of problems closely matches the challenges faced by natural evolution in generating biological GRNs. Starting with an existing GRN model, the fractal GRN (FGRN) model, a successful application to a standard control problem will be presented, followed by multiple improvements to the FGRN model and its associated genetic algorithm, resulting in better performances in terms of both reliability and speed. Limitations will be identified in the FGRN model, leading to the introduction of the Input-Merge- Regulate-Output (IMRO) architecture for GRN models, an implementation of which will show both quantitative and qualitative improvements over the FGRN model, solving harder control problems. The resulting model also displays useful features which should facilitate further extension and real-world use of the system