Software Self-Adaptability by Means of Artificial Evolution

Abstract

Abstract. Contemporary distributed software systems are reaching extremely high complexity levels which exceeds complexities of known engineering problems to date. Especially open heterogeneous multi-agent systems which may potentially be spread all around the globe, interacting with different changing web-services and web-technologies are exposed to demanding, dynamic and highly unpredictable environments. Traditional control-based handling of adaptability may not be suitable anymore, therefore there is a tendency for exploring different adaptability models inspired by natural/biological phenomena. In this article we review overall design of an adaptive software system based on a simple model of artificial evolution. We propose a new paradigm for handling complexity in dynamic environments based on a theory of self-producing self-adaptive software systems. We have substantial evidence to believe that a bottomup approach based on self-production and self-maintenance may help to build more robust and more flexible self-adapting software systems. This paper introduces the new framework, provides analysis of some results, implications and future research directions toward a complete and selfcontained theory of evolvable and self-adaptable software systems.

Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains

“…There was a major parallel between autonomic computing, self-* computing, and biological systems, and a branch of research was formed to gain inspiration from nature to bring to these systems [125], [140], [197]. As a precursor to the cloud based systems of the present day, load balancing was an area of research focus.…”