Finite Markov chain analysis of genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Analyzing stability in wide-area network performance
SIGMETRICS '97 Proceedings of the 1997 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Finite Markov chain results in evolutionary computation: a tour d'horizon
Fundamenta Informaticae
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
The Stability, Scalability and Performance of Multi-agent Systems
BT Technology Journal
Nature-Inspired Computing Technology and Applications
BT Technology Journal
Global Convergence of Genetic Algorithms: A Markov Chain Analysis
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Heterogeneity, Stability, and Efficiency in Distributed Systems
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Embodiment of Evolutionary Computation in General Agents
Evolutionary Computation
Digital ecosystems: evolving service-orientated architectures
Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems
Mobile software agents: an overview
IEEE Communications Magazine
An ecologically inspired simulation tool for managing digital ecosystems
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
A two-layer ecosystem evolution model: lessons from stages of mobile data services
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Managing a digital business ecosystem using a simulation tool
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
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Stability is perhaps one of the most desirable features of any engineered system, given the importance of being able to predict its response to various environmental conditions prior to actual deployment. Engineered systems are becoming ever more complex, approaching the same levels of biological ecosystems, and so their stability becomes ever more important, but taking on more and more differential dynamics can make stability an ever more elusive property. The Chli-DeWilde definition of stability views a Multi-Agent System as a discrete time Markov chain with potentially unknown transition probabilities. With a Multi-Agent System being considered stable when its state, a stochastic process, has converged to an equilibrium distribution, because stability of a system can be understood intuitively as exhibiting bounded behaviour. We investigate an extension to include Multi-Agent Systems (MASs) with evolutionary dynamics, focusing on the evolving agent populations of our Digital Ecosystem. We then built upon this to construct an entropy-based definition for the degree of instability (entropy of the limit probabilities), which was later used to perform a stability analysis. The Digital Ecosystem is considered to investigate the stability of an evolving agent population through simulations, for which the results were consistent with the original Chli-DeWilde definition.