You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Gossipping has demonstrate to be an efficient mechanism for spreading information among P2P networks. Within the context of P2P computing, we propose the so-called Evolvable Agent Model for distributed population-based algorithms which uses gossipping as communication policy, and represents every individual as a self-scheduled single thread. The model avoids obsolete nodes in the population by defining a self-adaptive refresh rate which depends on the latency and bandwidth of the network. Such a mechanism balances the migration rate to the congestion of the links pursuing global population coherence. We perform an experimental evaluation of this model on a real parallel system and observe how solution quality and algorithm speed scale with the number of processors with this seamless approach.

Cited Abstract(s):
[1]: The Internet is different from traditional parallel computing environments, and Distributed Genetic Algorithms (DGAs) for the Internet need to be designed to address these differences. This paper presents a framework for Internet island model DGAs that uses adaptation methods to maintain efficiency and robustness in a volatile and dynamic run-time environment. The applicability of the methods is demonstrated on benchmark tests, and a real-world optimization problem in VLSI design.

[2]: This paper describes the recently released DREAM (Distributed Resource Evolutionary Algorithm Machine) framework for the automatic distribution of evolutionary algorithm (EA) processing through a virtual machine built from large numbers of individual machines linked by standard Internet protocols. The framework allows five different user entry points which depend on the knowledge and requirements of the user. At the highest level, users may specify and run distributed EAs simply by manipulating graphical displays. At the lowest level the framework turns becomes a P2P (Peer to Peer) mobile agent system, that may be used for the automatic distribution of a class of processes including, but not limited to, EAs.
Concerning development of P2P distributed computing systems, there are some frameworks such as: DREAM [2] , which focuses in distributed processing of EAs and uses the P2P network DRM. G2DGA [1] , equivalent to the previous. It centers on distributed genetic algorithms processing by the use of the network G2P2P. JADE (Java Agent Development Framework, available from http: jade.cselt.it ), a P2P system which includes agents as software components.