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

Self-organization of Symbiotic Multicellular Structures

This paper presents a new model for the development of artificial creatures from a single cell. The model aims at providing a more biologically plausible abstraction of the morphogenesis and the specialization process, which the organogenesis follows. It is built upon three main elements: a cellular physics system that simulates division and intercellular adhesion dynamics, a simplified cell cycle offering to the cells the possibility to select actions such as division, quiescence, differentiation or apoptosis and, finally, a cell specialization mechanism quantifying the ability to perform different functions. An evolved artificial gene regulatory network is employed as a cell controller. As a proof-of-concept, we present two experiments where the morphology of a multicellular organism is guided by cell weaknesses and efficiency at performing different functions under environmental stress

Open-ended Search through Minimal Criterion Coevolution

Search processes guided by objectives are ubiquitous in machine learning. They iteratively reward artifacts based on their proximity to an optimization target, and terminate upon solution space convergence. Some recent studies take a different approach, capitalizing on the disconnect between mainstream methods in artificial intelligence and the field\u27s biological inspirations. Natural evolution has an unparalleled propensity for generating well-adapted artifacts, but these artifacts are decidedly non-convergent. This new class of non-objective algorithms induce a divergent search by rewarding solutions according to their novelty with respect to prior discoveries. While the diversity of resulting innovations exhibit marked parallels to natural evolution, the methods by which search is driven remain unnatural. In particular, nature has no need to characterize and enforce novelty; rather, it is guided by a single, simple constraint: survive long enough to reproduce. The key insight is that such a constraint, called the minimal criterion, can be harnessed in a coevolutionary context where two populations interact, finding novel ways to satisfy their reproductive constraint with respect to each other. Among the contributions of this dissertation, this approach, called minimal criterion coevolution (MCC), is the primary (1). MCC is initially demonstrated in a maze domain (2) where it evolves increasingly complex mazes and solutions. An enhancement to the initial domain (3) is then introduced, allowing mazes to expand unboundedly and validating MCC\u27s propensity for open-ended discovery. A more natural method of diversity preservation through resource limitation (4) is introduced and shown to maintain population diversity without comparing genetic distance. Finally, MCC is demonstrated in an evolutionary robotics domain (5) where it coevolves increasingly complex bodies with brain controllers to achieve principled locomotion. The overall benefit of these contributions is a novel, general, algorithmic framework for the continual production of open-ended dynamics without the need for a characterization of behavioral novelty

A Synthesis of the Cell2Organ Developmental Model

Over the past twenty years, many techniques have appeared to simulate artificial creatures at different scales: starting with the simulation of their behaviour at the beginning of the 90s, researchers have continued by modifying the robots’ morphologies to adapt them to their environment. More recently, developmental mechanisms of living beings have inspired artificial embryogenesis to generate smaller creatures composed of tens to thousands of cells. However, we observe that no traversal model, able to simulate creatures at these different scales, exists. Starting from a unique cell, our project’s goal is to develop a complete creature, which contains different organs and high-level functionalities. Thus, we propose a developmental model based on three layers of simulation. The first one consists in a chemical environment where cells can divide and manipulate substrates and chemical reactions. The aim is to develop a metabolism adapted to the environment. Often forgotten in classical models, this is crucial in all living systems. It allows every organism to perform actions in its environment with accumulated energy. Our developmental model also includes a physic layer that allows the creatures to produce global motion in a Newtonian world. Cells can here modify their shape to modify the shape of the organism. A hydrodynamic layer simulates substrate flows in the environment so that the cells can modify the whole environment. Finally, we propose a new method to get rid of the molecular morphogens by the mean of a L-System driven morphogenesis

A Synthesis of the Cell2Organ Developmental Model

Over the past twenty years, many techniques have appeared to simulate artificial creatures at different scales: starting with the simulation of their behaviour at the beginning of the 90s, researchers have continued by modifying the robots’ morphologies to adapt them to their environment. More recently, developmental mechanisms of living beings have inspired artificial embryogenesis to generate smaller creatures composed of tens to thousands of cells. However, we observe that no traversal model, able to simulate creatures at these different scales, exists. Starting from a unique cell, our project’s goal is to develop a complete creature, which contains different organs and high-level functionalities. Thus, we propose a developmental model based on three layers of simulation. The first one consists in a chemical environment where cells can divide and manipulate substrates and chemical reactions. The aim is to develop a metabolism adapted to the environment. Often forgotten in classical models, this is crucial in all living systems. It allows every organism to perform actions in its environment with accumulated energy. Our developmental model also includes a physic layer that allows the creatures to produce global motion in a Newtonian world. Cells can here modify their shape to modify the shape of the organism. A hydrodynamic layer simulates substrate flows in the environment so that the cells can modify the whole environment. Finally, we propose a new method to get rid of the molecular morphogens by the mean of a L-System driven morphogenesis