Edge sets: an effective evolutionary coding of spanning trees

Raidl, Günther R.; Julstrom, Bryant A.

doi:10.1109/tevc.2002.807275

Cited by 240 publications

(177 citation statements)

References 42 publications

(49 reference statements)

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“…O número de árvores geradoras em um grafo completo é da ordem de m m-2 , m = |N| (Raidl & Julstrom, 2001). Por exemplo, de um grafo completo com 50 nós é possível obter 50 48 ≈ 3,55x10 81 árvores possíveis.…”

Section: Motivaçãounclassified

Sistema imunológico artificial para resolver o problema da árvore geradora mínima com parâmetros fuzzy

2007

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ResumoNeste trabalho é proposta uma meta-heurística baseada em técnicas da computação evolutiva que visa encontrar um conjunto de árvores geradoras mínimas, para problemas de grafos que possuem incertezas em relação às informações associadas aos parâmetros. Resolver problemas dessa natureza é um processo NP-Completo, pois envolve um número enorme de comparações. A fim de contornar essa complexidade, é proposto um sistema imunológico artificial capaz de explorar eficientemente o espaço de busca e de obter resultados satisfatórios, sem a necessidade de confrontar todas as soluções entre si. Palavras-chave: computação evolutiva; grafos fuzzy; programação matemática fuzzy. AbstractThis work proposes an heuristical approach based on evolutionary computation, whose goal is to find a set of minimum spanning trees in graphs that contain uncertainties in their parameters. This kind of problem is a NP-Hard one, because it involves an enormous number of comparisons. In order to avoid this complexity, this work proposes an artificial immune system that explores efficiently the search space of solutions to looking for satisfactory results, without the necessity of comparing all possible solutions.

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Section: Motivaçãounclassified

Sistema imunológico artificial para resolver o problema da árvore geradora mínima com parâmetros fuzzy

2007

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“…The edge-set encoding [3] is a direct representation for trees. Therefore, the search operators are applied directly to sets of edges.…”

Section: The Edge-set Encodingmentioning

confidence: 99%

“…In the following paragraphs we briefly review the functionality of the initialization method and the crossover operator. We do not consider the mutation operator as [3] already proposed a version of the mutation operator that allows us to control its bias (compare [7]). …”

Section: The Edge-set Encodingmentioning

confidence: 99%

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Making the Edge-Set Encoding Fly by Controlling the Bias of Its Crossover Operator

Rothlauf

Tzschoppe

2005

Evolutionary Computation in Combinatorial Optimization

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The edge-set encoding is a direct tree encoding which applies search operators directly to trees represented as sets of edges. There are two variants of crossover operators for the edge-set encoding: With heuristics that consider the weights of the edges, or without heuristics. Due to a strong bias of the heuristic crossover operator towards the minimum spanning tree (MST) a population of solutions converges quickly towards the MST and EAs using this operator show low performance when used for tree optimization problems where the optimal solution is not the MST. This paper presents a modified crossover operator (γ-TX) that allows us to control the bias towards the MST. The bias can be set arbitrarily between the strong bias of the heuristic crossover operator, or being unbiased. An investigation into the performance of EAs using the γ-TX for randomly created OCST problems of different types and OCST test instances from the literature present good results when setting the crossover-specific parameter γ properly. The presented results suggest that the original heuristic crossover operator of the edge-sets should be substituted by the modified γ-TX operator that allows us to control the bias towards the MST.

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“…G. Raidl and B.A. Julstrom in [7] proposed Edge Sets: An Effective Evolutionary Coding of Spanning Trees. Zhou and Gen presented a note on genetic algorithm approach to the degree-constrained spanning tree problems in [8], [9], [22].…”

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A Novel PSO Based Algorithm Approach for the cMTS to Improve QoS in Next Generation Networks

Nguyen¹,

Le²,

Le³

et al. 2013

IJFCC

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Abstract-In this paper, we propose an effective Particle Swarm Optimization (PSO) algorithm to solving the capacitated minimum spanning tree (cMTS) problem to improve Quality of Service (QoS) in Next Generation Network (NGN). To improving QoS of communication network with considering the network provisioning capability and dynamic environment, we formulate this problem with minimizing the communication cost (as a kind of performance measures for network's QoS). We calculate the fitness value of each scheme and update them step by step with the best method to quickly find good approximate solutions of cMST problems. Numerical experiments show that our algorithm proposed have achieved much better than recent researches.Index Terms-Capacitated minimum spanning tree, communication network, quality of service, next generation network, particle swarm optimization.

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Edge sets: an effective evolutionary coding of spanning trees

Cited by 240 publications

References 42 publications

Sistema imunológico artificial para resolver o problema da árvore geradora mínima com parâmetros fuzzy

Sistema imunológico artificial para resolver o problema da árvore geradora mínima com parâmetros fuzzy

Making the Edge-Set Encoding Fly by Controlling the Bias of Its Crossover Operator

A Novel PSO Based Algorithm Approach for the cMTS to Improve QoS in Next Generation Networks

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