Evolutionary Growth of Genome Representations on Artificial Cellular Organisms with Indirect Encodings

Abstract

Evolutionary design targets systems of continuously increasing complexity. Thus, indirect developmental mappings are often a necessity. Varying the amount of genotype information changes the cardinality of the mapping, which in turn affects the developmental process. An open question is how to find the genotype size and representation in which a developmental solution would fit. A restricted pool of genes may not be large enough to encode a solution or may need complex heuristics to find a realistic size. On the other hand, using the whole set of possible regulatory combinations may be intractable. In nature, the genomes of biological organisms are not fixed in size; they slowly evolve and acquire new genes by random gene duplications. Such incremental growth of genome information can be beneficial also in the artificial domain. For an evolutionary and developmental (evo-devo) system based on cellular automata, we investigate an incremental evolutionary growth of genomes without any a priori knowledge on the necessary genotype size. Evolution starts with simple solutions in a low-dimensional space and incrementally increases the genotype complexity by means of gene duplication, allowing the evolution of scalable genomes that are able to adapt genetic information content while compactness and efficiency are retained. The results are consistent when the target phenotypic complexity, the geometry size, and the number of cell states are scaled up.

CA-NEAT: Evolved Compositional Pattern Producing Networks for Cellular Automata Morphogenesis and Replication

“…Nichele and Tufte [58] investigated an instruction based encoding where genomes could complexify during evolution. In [59] traditional CA transition functions are evolved through complexification. Nichele et al [60] also proposed an instruction-based development with instructions that could self-modify the genome program during evolution.…”

Connectomics of Morphogenetically Engineered Neurons as a Predictor of Functional Integration in the Ischemic Brain

“…RNNs are thus well-suited to capturing dynamic network behavior of in vitro neural networks, including their critical states. These attributes of RNNs make them an excellent computational paradigm for the investigation of mechanisms by which neuronal populations, including engineered neural networks, solve various computational problems in healthy and perturbed conditions (178–182). In this way, we can start addressing fundamental questions that can help determine the functional capacity of engineered neurons.…”