16,102 research outputs found
Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks
This article is posted here with permission from the IEEE - Copyright @ 2009 IEEEIn recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless sensor network (WSN), etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem (DOP) in MANETs. In this paper, we propose to use elitism-based immigrants GA (EIGA) to solve the dynamic SP problem in MANETs. We consider MANETs as target systems because they represent new generation wireless networks. The experimental results show that the EIGA can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1
Evolutionary Synthesis of Analog Electronic Circuits Using EDA Algorithms
DisertaÄnĂ prĂĄce je zamÄĹena na nĂĄvrh analogovĂ˝ch elektronickĂ˝ch obvodĹŻ pomocĂ algoritmĹŻ s pravÄpodobnostnĂmi modely (algoritmy EDA). PrezentovanĂŠ metody jsou na zĂĄkladÄ poĹžadovanĂ˝ch charakteristik cĂlovĂ˝ch obvodĹŻ schopny navrhnout jak parametry pouĹžitĂ˝ch komponent tak takĂŠ jejich topologii zapojenĂ. TĹi rĹŻznĂŠ metody vyuĹžitĂ EDA algoritmĹŻ jsou navrĹženy a otestovĂĄny na pĹĂkladech skuteÄnĂ˝ch problĂŠmĹŻ z oblasti analogovĂ˝ch elektronickĂ˝ch obvodĹŻ. PrvnĂ metoda je urÄena pro nĂĄvrh pasivnĂch analogovĂ˝ch obvodĹŻ a vyuĹžĂvĂĄ algoritmus UMDA pro nĂĄvrh jak topologie zapojenĂ tak takĂŠ hodnot parametrĹŻ pouĹžitĂ˝ch komponent. Metoda je pouĹžita pro nĂĄvrh admitanÄnĂ sĂtÄ s poĹžadovanou vstupnĂ impedancĂ pro ĂşÄely chaotickĂŠho oscilĂĄtoru. DruhĂĄ metoda je takĂŠ urÄena pro nĂĄvrh pasivnĂch analogovĂ˝ch obvodĹŻ a vyuĹžĂvĂĄ hybridnĂ pĹĂstup - UMDA pro nĂĄvrh topologie a metodu lokĂĄlnĂ optimalizace pro nĂĄvrh parametrĹŻ komponent. TĹetĂ metoda umoĹžĹuje nĂĄvrh analogovĂ˝ch obvodĹŻ obsahujĂcĂch takĂŠ tranzistory. Metoda vyuĹžĂvĂĄ hybridnĂ pĹĂstup - EDA algoritmus pro syntĂŠzu topologie a metoda lokĂĄlnĂ optimalizace pro urÄenĂ parametrĹŻ pouĹžitĂ˝ch komponent. Informace o topologii je v jednotlivĂ˝ch jedincĂch populace vyjĂĄdĹena pomocĂ grafĹŻ a hypergrafĹŻ.Dissertation thesis is focused on design of analog electronic circuits using Estimation of Distribution Algorithms (EDA). Based on the desired characteristics of the target circuits the proposed methods are able to design the parameters of the used components and theirs topology of connection as well. Three different methods employing EDA algorithms are proposed and verified on examples of real problems from the area of analog circuits design. The first method is capable to design passive analog circuits. The method employs UMDA algorithm which is used for determination of the parameters of the used components and synthesis of the topology of their connection as well. The method is verified on the problem of design of admittance network with desired input impedance function which is used as a part of chaotic oscillator circuit. The second method is also capable to design passive analog circuits. The method employs hybrid approach - UMDA for synthesis of the topology and local optimization method for determination of the parameters of the components. The third method is capable to design analog circuits which include also ac- tive components such as transistors. Hybrid approach is used. The topology is synthesized using EDA algorithm and the parameters are determined using a local optimization method. In the individuals of the population information about the topology is represented using graphs and hypergraphs.
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems
Majority of Artificial Neural Network (ANN) implementations in autonomous
systems use a fixed/user-prescribed network topology, leading to sub-optimal
performance and low portability. The existing neuro-evolution of augmenting
topology or NEAT paradigm offers a powerful alternative by allowing the network
topology and the connection weights to be simultaneously optimized through an
evolutionary process. However, most NEAT implementations allow the
consideration of only a single objective. There also persists the question of
how to tractably introduce topological diversification that mitigates
overfitting to training scenarios. To address these gaps, this paper develops a
multi-objective neuro-evolution algorithm. While adopting the basic elements of
NEAT, important modifications are made to the selection, speciation, and
mutation processes. With the backdrop of small-robot path-planning
applications, an experience-gain criterion is derived to encapsulate the amount
of diverse local environment encountered by the system. This criterion
facilitates the evolution of genes that support exploration, thereby seeking to
generalize from a smaller set of mission scenarios than possible with
performance maximization alone. The effectiveness of the single-objective
(optimizing performance) and the multi-objective (optimizing performance and
experience-gain) neuro-evolution approaches are evaluated on two different
small-robot cases, with ANNs obtained by the multi-objective optimization
observed to provide superior performance in unseen scenarios
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/
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