Evolvability is the capacity of a genotype to rapidly adjust to certain types of environmental challenges or opportunities. This capacity, documented in nature, reflects foresight enabled by the capacity of evolution to capture and represent regularities not only in extant environments, but in the ways in which the environments tend to change. Here we posit that evolvability substantially benefits from the hierarchical representations afforded by Gene Regulatory Networks (GRNs). We present an extension of standard Genetic Algorithms (GAs) and demonstrate its capacity to learn a genotype phylogeny able to express rapid phenotypic shifts in the context of an oscillating environment.
|Title of host publication||The 14th International Conference on the Synthesis and Simulation of Living Systems (ALife 2014)|
|Publication status||Published - 2014|