Abstract
One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store ‘good behaviour’ and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment.
Highlights
An important goal in evolutionary robotics is the development of systems that show self-adaptation in dynamically changing environments [1,2]
We assume that the genomic encoding of the cellular regulatory network and the way this encoding is translated into an activated gene regulatory network (GRN) is a feature of natural systems that is key to flexible and robust adaptation
We have calculated the rate of the average energy increase from the start of the environment reset to the environment reset
Summary
An important goal in evolutionary robotics is the development of systems that show self-adaptation in dynamically changing environments [1,2]. A truly selfadaptive system should reach higher performance in one particular environment, but should evolve a better selfinnovating ability that allows it to survive under different and changing conditions. The molecular mechanisms underlying the adaptability of biological systems are Gene Regulatory Networks (GRNs), which are composed of interacting genetic entities such as genes and proteins [7,8,9]. These networks transduce signals rising from environmental cues into a proper phenotypic behaviour that allows the organism to flexibly respond to environmental changes. Evolutionary processes acting on this genome gradually can lead to novel emerging circuits (evolutionary network rewiring)
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