ABSTRACT
To increase the evolvability of larger search spaces, several indirect encoding strategies have been proposed. Among these, multicellular developmental systems are believed to offer great potential for the evolution of general, scalable, and self-repairing organisms. We reinforce this view, presenting the results achieved by such a model and comparing it against direct encoding. Extra effort has been made to make this comparison both general and meaningful. Embryonal stages, a generic method showing increased evolvability and applicable to any developmental model, are introduced. Development with embryonal stages implements what we refer to as direct neutral complexification: direct genotype complexification by neutral duplication of expressed genes. The results show that, even for high-complexity evolutionary targets, the developmental model proves more scalable. The model also shows emergent self-repair, which is used to produce highly resilient organisms.
Subject(s)
Biological Evolution , Models, Biological , Algorithms , Cell Proliferation , Embryonic Development , Gene Duplication , Models, Genetic , Mutation , PhenotypeABSTRACT
Due to their distributed architecture, artificial neural networks often show a graceful performance degradation to the loss of few units or connections. Living systems also display an additional source of fault-tolerance obtained through distributed processes of self-healing: defective components are actively regenerated. In this paper, we present results obtained with a model of development for spiking neural networks undergoing sustained levels of cell loss. To test their resistance to faults, networks are subjected to random faults during development and mutilated several times during operation. Results show that, evolved to control simulated Khepera robots in a simple navigation task, plastic and non-plastic networks develop fault-tolerant structures which can recover normal operation to various degrees.
