Modelling biological processes naturally using systemic computation: genetic algorithms, neural networks, and artificial immune systems

Natural systems provide unique examples of computation in a form very different from contemporary computer architectures. Biology also demonstrates capabilities such as adaptation, self-repair and self-organisation that are becoming increasingly desirable for our technology. To address these issues a computer model and architecture with natural characteristics is presented. Systemic computation is Turing Complete; it is designed to support biological algorithms such as neural networks, evolutionary algorithms and models of development, and shares the desirable capabilities of biology not found in conventional architectures. In this chapter we describe the first platform implementing such computation, including programming language, compiler and virtual machine. We first demonstrate that systemic computing is crash-proof and can recover from severe damage. We then illustrate various benefits of systemic computing through several implementations of bio-inspired algorithms: a self-adaptive genetic algorithm, a bio-inspired model of artificial neural networks, and finally we create an "artificial organism "- a program with metabolism that eats data, expels waste, clusters cells based on data inputs and emits danger signals for a potential artificial immune system. Research on systemic computation is still ongoing, but the research presented in this chapter shows that computers that process information according to this bio-inspired paradigm have many of the features of natural systems that we desire