Neuroevolution and complexifying genetic architectures for memory and control tasks.
The way genes are interpreted biases an artificial evolutionary system towards some phenotypes. When evolving artificial neural networks, methods using direct encoding have genes representing neurons and synapses, while methods employing artificial ontogeny interpret genomes as recipes for the construction of phenotypes. Here, a neuroevolution system (neuroevolution with ontogeny or NEON) is presented that can emulate a well-known neuroevolution method using direct encoding (neuroevolution of augmenting topologies or NEAT), and therefore, can solve the same kinds of tasks. Performance on challenging control and memory benchmark tasks is reported. However, the encoding used by NEON is indirect, and it is shown how characteristics of artificial ontogeny can be introduced incrementally in different phases of evolutionary search.
Inden
Benjamin
B
Max Planck Institute for Mathematics in the Sciences, Inselstr. 22, 04103, Leipzig, Germany. inden@mis.mpg.de
D000818:Animals; D005075:Biological Evolution; D001921:Brain / Q000502:physiology*; D006801:Humans; D008568:Memory / Q000502:physiology*; D008957:Models, Genetic; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009419:Nerve Tissue Proteins / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology*; D013647:Task Performance and Analysis
D009419:Nerve Tissue Proteins