50,288 research outputs found
Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks
This article is posted here with permission of IEEE - Copyright @ 2010 IEEEIn recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. In this paper, we propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. We consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.This work was supported by the Engineering
and Physical Sciences Research Council of U.K. underGrant EP/E060722/
Population-based incremental learning with memory scheme for changing environments
Copyright @ 2005 ACMIn recent years there has been a growing interest in studying evolutionary algorithms for dynamic optimization problems due to its importance in real world applications. Several approaches have been developed, such as the memory scheme. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic optimization problems. A PBIL-specific memory scheme is proposed to improve its adaptability in dynamic environments. In this memory scheme the working probability vector is stored together with the best sample it creates in the memory and is used to reactivate old environments when change occurs. Experimental study based on a series of dynamic environments shows the efficiency of the memory scheme for PBILs in dynamic environments. In this paper, the relationship between the memory scheme and the multipopulation scheme for PBILs in dynamic environments is also investigated. The experimental results indicate a negative interaction of the multi-population scheme on the memory scheme for PBILs in the dynamic test environments
On the performance of a hybrid genetic algorithm in dynamic environments
The ability to track the optimum of dynamic environments is important in many
practical applications. In this paper, the capability of a hybrid genetic
algorithm (HGA) to track the optimum in some dynamic environments is
investigated for different functional dimensions, update frequencies, and
displacement strengths in different types of dynamic environments. Experimental
results are reported by using the HGA and some other existing evolutionary
algorithms in the literature. The results show that the HGA has better
capability to track the dynamic optimum than some other existing algorithms.Comment: This paper has been submitted to Applied Mathematics and Computation
on May 22, 2012 Revised version has been submitted to Applied Mathematics and
Computation on March 1, 201
High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm
We implement a master-slave parallel genetic algorithm (PGA) with a bespoke
log-likelihood fitness function to identify emergent clusters within price
evolutions. We use graphics processing units (GPUs) to implement a PGA and
visualise the results using disjoint minimal spanning trees (MSTs). We
demonstrate that our GPU PGA, implemented on a commercially available general
purpose GPU, is able to recover stock clusters in sub-second speed, based on a
subset of stocks in the South African market. This represents a pragmatic
choice for low-cost, scalable parallel computing and is significantly faster
than a prototype serial implementation in an optimised C-based
fourth-generation programming language, although the results are not directly
comparable due to compiler differences. Combined with fast online intraday
correlation matrix estimation from high frequency data for cluster
identification, the proposed implementation offers cost-effective,
near-real-time risk assessment for financial practitioners.Comment: 10 pages, 5 figures, 4 tables, More thorough discussion of
implementatio
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