A scalable genome representation for neural-symbolic networks

Neural networks that are capable of representing symbolic information such as logic programs are said to be neural-symbolic. Because the human mind is composed of interconnected neurons and is capable of storing and processing symbolic information, neural-symbolic networks contribute towards a model of human cognition. Given that natural evolution and development are capable of producing biological networks that are able to process logic, it may be possible to produce their artificial counterparts through evolutionary algorithms that have developmental properties. The first step towards this goal is to design a genome representation of a neural-symbolic network. This paper presents a genome that directs the growth of neural-symbolic networks constructed according to a model known as SHRUTI. The genome is successful in producing SHRUTI networks that learn to represent relations between logical predicates based on observations of sequences of predicate instances. A practical advantage of the genome is that its length is independent of the size of the network it encodes, because rather than explicitly encoding a network topology, it encodes a set of developmental rules. This approach to encoding structure in a genome also has biological grounding

Ontogeny tends to recapitulate phylogeny in digital organisms

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. . abstract: Biologists have long debated whether ontogeny recapitulates phylogeny and, if so, why. Two plausible explanations are that (i) changes to early developmental stages are selected against because they tend to disrupt later development and (ii) simpler structures often precede more complex ones in both ontogeny and phylogeny if the former serve as building blocks for the latter. It is difficult to test these hypotheses experimentally in natural systems, so we used a computational system that exhibits evolutionary dynamics. We observed that ontogeny does indeed recapitulate phylogeny; traits that arose earlier in a lineage's history also tended to be expressed earlier in the development of individuals. The relative complexity of traits contributed substantially to this correlation, but a significant tendency toward recapitulation remained even after accounting for trait complexity. This additional effect provides evidence that selection against developmental disruption also contributed to the conservation of early stages in development

Evolving Artificial Neural Networks that Develop in Time

Although recently there has been an increasing interest in studing genetically-based development using Artificial Life models, the mapping of the genetic information into the phenotype is usually modeled as an abstract process that takes place instantaneously, i.e. before the creature starts to interact with the external world and is tested for fitness. In this paper we show that the temporal dimension of development has important consequences. By analyzing the results of simulations with temporally developing neural netwoks we found that evolution, by favouring the reproduction of Os which are efficient at all epochs of their life, selects genotypes which dictate early maturation of functional neural structure but not of nonfunctional structure. In addition, we found that development in time forces evolution to be conservative with characters that mature in the first phases of development while it allows evolution to play more freely with characters that mature later in development. F..