Bernhard Sendhoff

Emerged coupling of motor control and morphological development in evolution of multi-cellular animats

A model for co-evolving behavior control and morphological development is presented in this paper. The development of the morphology starts with a single cell that is able to divide or die, which is controlled by a gene regulatory network. The cells are connected by springs and form the morphology of the grown individuals. The movements of animats are resulted from the shrinking and relaxation of the springs connecting the lateral cells on the body morphology. The gene regulatory network, together with the frequency and phase shifts of the spring movements are evolved to maximize the distance that the animats can swim in a given time interval. To facilitate the evolution of swimming animats, a term that awards an elongated morphology is also included in the fitness function. We show that animats with different body-plans emerge in the evolutionary runs and that the evolved movement control strategy is coupled with the body plan.

A gene regulatory model for the development of primitive nervous systems

This paper presents a model for the development of primitive nervous systems in a hydra-like animat controlled by a gene regulatory network. The gene regulatory network consists of structural genes that simulate the main cellular events during neural development at an abstract level, namely, cell division, cell migration, and axon growth, and regulatory genes that control the expression of the structural genes. The developmental model is evolved with an evolutionary algorithm to achieve the correct developmental order. After the genetically controlled neural development is completed, the connectivity and weights of the neural networks are further adapted to allow the animat for performing simple behaviors such as the food catching behavior of a hydra. Our preliminary results suggest that the proposed developmental model is promising for computational simulation of the evolution of neural development for understanding neural organization in biological organisms.

Emergence of feedback in artificial gene regulatory networks

In this paper, we present a model for simulating the evolution of development together with a method for the analysis of emergence of negative feedback inside the regulatory network. In order to record the development of feedback during evolution, we analyze both the static as well as the dynamic interactions between the transcription factors in the regulatory network. When perturbing the gene regulatory network using random mutations, we find that the evolved negative feedback is the main mechanism for robustness against such mutations. We argue that this robustness is the reason for the sustained emergence of negative feedback during evolution.

Redundancy creates opportunity in developmental representations

This paper investigates the influence of redundancy on the evolutionary performance of a gene regulatory network governing a cellular growth process. Redundancy is believed to play a key role in robustness and evolvability of biological systems. We use a cellular model controlled by a gene regulatory network to evolve elongated morphologies. We show that removing the redundancy in the genome during the evolution decreases the performance of the evolution strategy. A comparing run with few parameters and therefore no redundancy performs worst, which supports the hypothesis that redundancy improves evolvability