A genetic approach for long term virtual organization distribution

Electronic versíon of an article published as International Journal on Artificial Intelligent Tools, Volume 20, issue 2, 2011. 10.1142/S0218213011000152. © World Scientific Publishing Company[EN] An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios. © 2011 World Scientific Publishing Company.This work is supported by TIN2008-04446, TIN2009-13839-C03-01, CSD2007-00022 and FPU grant AP2008-00600 of the Spanish government, and PROMETEO 2008/051 of the Generalitat Valenciana.Sánchez Anguix, V.; Valero Cubas, S.; García Fornes, AM. (2011). A genetic approach for long term virtual organization distribution. International Journal on Artificial Intelligence Tools. 20(2):271-295. https://doi.org/10.1142/S0218213011000152S27129520

A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE

A major objective of the 3GPP LTE standard is the provision of high-speed data services. These services must be guaranteed under varying radio propagation conditions, to stochastically distributed mobile users. A necessity for determining and regulating the traffic load of eNodeBs naturally ensues. Load balancing is a self-optimization operation of self-organizing networks (SON). It aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. Most of the algorithms are based on hard (traditional) computing which does not utilize the tolerance for precision of load balancing. This paper proposes the use of soft computing, precisely adaptive Neuro-fuzzy inference system (ANFIS) model for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuitio

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Genetics has long been used as a source of evidence to understand domestication origins. A recent shift in the emphasis of archaeological evidence from a rapid transition paradigm of hunter-gatherers to agriculturalists, to a protracted transition paradigm has highlighted how the scientific framework of interpretation of genetic data was quite dependent on archaeological evidence, resulting in a period of discord in which the two evidence types appeared to support different paradigms. Further examination showed that the discriminatory power of the approaches employed in genetics was low, and framed within the rapid paradigm rather than testing it. In order to interpret genetic data under the new protracted paradigm it must be taken into account how that paradigm changes our expectations of genetic diversity. Preliminary examination suggests that a number of features that constituted key evidence in the rapid paradigm are likely to be interpreted very differently in the protracted paradigm. Specifically, in the protracted transition the mode and mechanisms involved in the evolution of the domestication syndrome have become much more influential in the shape of genetic diversity. The result is that numerous factors interacting over several levels of organization in a domestication system need to be taken into account in order to understand the evolution of the process. This presents a complex problem of integration of different data types which is difficult to describe formally. One possible way forward is to use Bayesian approximation approaches that allow complex systems to be measured in a way that does not require such formality

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Ant algorithms are inspired in real ants and the main idea is to create virtual ants that travel into the space of possible solution depositing virtual pheromone proportional to how good a specific solution is. This creates a autocatalytic (positive feedback) process that can be used to generate automatic solutions to very difficult problems. In the present work we show that these algorithms can be used coupled to tight-binding hamiltonians to design conducting polymers with pre-specified properties. The methodology is completely general and can be used for a large number of optimization problems in materials science

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