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20
A gentle introduction to memetic algorithms
 Handbook of Metaheuristics
, 2003
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Redundant representations in evolutionary computation
 EVOLUTIONARY COMPUTATION
, 2003
"... This paper investigates how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and nonsynonymously redundant ..."
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Cited by 27 (2 self)
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This paper investigates how the use of redundant representations influences the performance of genetic and evolutionary algorithms. Representations are redundant if the number of genotypes exceeds the number of phenotypes. A distinction is made between synonymously and nonsynonymously redundant representations. Representation are synonymously redundant if the genotypes that represent the same phenotype are very similar to each other. Nonsynonymously redundant representations do not allow genetic operators to work properly and result in a lower performance of evolutionary search. When using synonymously redundant representations, the performance of selectorecombinative genetic algorithms (GAs) depends on the modification of the initial supply. Theoretical models are developed that show the necessary population size to solve a problem and the number of generations goes with O(2 /r), where k r is the order of redundancy and r is the number of genotypic building blocks (BB) that represent the optimal phenotypic BB. Therefore, uniformly redundant representations do not change the behavior of GAs. Only by increasing r, which means overrepresenting the optimal solution, does GA performance increase. Therefore, nonuniformly redundant representations can only be used advantageously if apriori information exists regarding the optimal solution. The validity of the proposed theoretical concepts is illustrated for the binary trivial voting mapping and the realvalued linkbiased encoding. The empirical investigations show that the developed population sizing and time to convergence models allow an accurate prediction of the empirical results.
An Efficient Evolutionary Algorithm for the DegreeConstrained Minimum Spanning Tree Problem
, 2000
"... The representation of candidate solutions and the variation operators are fundamental design choices in an evolutionary algorithm (EA). This paper proposes a novel representation technique and suitable variation operators for the degreeconstrained minimum spanning tree problem. For a weighted, undi ..."
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Cited by 23 (5 self)
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The representation of candidate solutions and the variation operators are fundamental design choices in an evolutionary algorithm (EA). This paper proposes a novel representation technique and suitable variation operators for the degreeconstrained minimum spanning tree problem. For a weighted, undirected graph G(V, E), this problem seeks to identify the shortest spanning tree whose node degrees do not exceed an upper bound d 2. Within the EA, a candidate spanning tree is simply represented by its set of edges. Special initialization, crossover, and mutation operators are used to generate new, always feasible candidate solutions. In contrast to previous spanning tree representations, the proposed approach provides substantially higher locality and is nevertheless computationally efficient; an offspring is always created in O(V time. In addition, it is shown how problemdependent heuristics can be effectively incorporated into the initialization, crossover, and mutation operators without increasing the timecomplexity. Empirical results are presented for hard problem instances with up to 500 vertices. Usually, the new approach identifies solutions superior to those of several other optimization methods within few seconds. The basic ideas of this EA are also applicable to other network optimization tasks.
EdgeSets: An Effective Evolutionary Coding of Spanning Trees
, 2002
"... The fundamental design choices in an evolutionary algorithm are its representation of candidate solutions and the operators that will act on that representation. We propose representing spanning trees in evolutionary algorithms for network design problems directly as sets of their edges, and we d ..."
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Cited by 15 (7 self)
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The fundamental design choices in an evolutionary algorithm are its representation of candidate solutions and the operators that will act on that representation. We propose representing spanning trees in evolutionary algorithms for network design problems directly as sets of their edges, and we describe initialization, recombination, and mutation operators for this representation. The operators offer
Peertopeer multipoint videoconferencing on the Internet Abstract
, 2005
"... A peertopeer architecture for multipoint videoconferencing is presented. Each conference participant may have asymmetric and dissimilar bandwidth connections to the Internet. The solution does not require additional hardware, as in multipoint control units, or network infrastructure support such a ..."
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Cited by 12 (2 self)
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A peertopeer architecture for multipoint videoconferencing is presented. Each conference participant may have asymmetric and dissimilar bandwidth connections to the Internet. The solution does not require additional hardware, as in multipoint control units, or network infrastructure support such as multicast. Without creating any additional demand on the networking and computing resources needed for a pointtopoint videoconference, this architecture can extend it into a multipoint one. A protocol for a completely distributed implementation has been developed and tested on a prototype system extending a pointtopoint video phone to a multipoint one. The architecture of the prototype system along with the details of the protocol optimization is discussed. Several performance results are presented.
A Comparison of Encodings and Algorithms for Multiobjective Minimum Spanning Tree Problems
 In Proceedings of the 2001 Congress on Evolutionary Computation (CEC'01
, 1997
"... this paper we apply (appropriately modified) the best of recent methods for the (degreeconstrained) single objective MST problem to the multiobjective MST problem, and compare with a method based on Zhou and Gen's approach. Our evolutionary computation approaches, using the different encodings ..."
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Cited by 7 (1 self)
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this paper we apply (appropriately modified) the best of recent methods for the (degreeconstrained) single objective MST problem to the multiobjective MST problem, and compare with a method based on Zhou and Gen's approach. Our evolutionary computation approaches, using the different encodings, involve a new populationbased variant of Knowles and Corne's PAES algorithm. We find the direct encoding to considerably outperform the Prufer encoding. And we find that a simple iterated approach, based on Prim's algorithm modified for the multiobjective MST, also significantly outperforms the Prufer encoding.
A Predecessor Coding in an Evolutionary Algorithm for the Capacitated Minimum Spanning Tree Problem
 Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference, pages 309–316, Las Vegas, NV
"... This article presents an evolutionary algorithm (EA) for the capacitated minimum spanning tree problem occurring in telecommunication applications. The EA encodes a solution by a predecessor vector indicating for each node the preceding node at the path to the given central root node. Initiali ..."
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Cited by 5 (3 self)
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This article presents an evolutionary algorithm (EA) for the capacitated minimum spanning tree problem occurring in telecommunication applications. The EA encodes a solution by a predecessor vector indicating for each node the preceding node at the path to the given central root node. Initialization, crossover, and mutation operators were specifically designed to provide strong locality and to enable an e#ective search in the space of feasible solutions only. Furthermore, local heuristics are applied to promote the inclusion of lowcost links. Empirical results on a set of standard test problems indicate that the EA performs better than two other heuristic techniques.
The Link and Node Biased Encoding Revisited: Bias and Adjustment of Parameters
, 2000
"... When using genetic and evolutionary algorithms (GEAs) for the optimal communication spanning tree problem, the design of a suitable tree network encoding is crucial for finding good solutions. The link and node biased (LNB) encoding represents the structure of a tree network using a weighted vector ..."
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Cited by 5 (0 self)
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When using genetic and evolutionary algorithms (GEAs) for the optimal communication spanning tree problem, the design of a suitable tree network encoding is crucial for finding good solutions. The link and node biased (LNB) encoding represents the structure of a tree network using a weighted vector and allows the GEA to distinguish between the importance of the nodes and links in the network. This paper investigates whether the encoding is unbiased in the sense that all trees are equally represented, and how the parameters of the encoding influence the bias. If the optimal solution is underrepresented in the population, a reduction in the GEA performance is unavoidable. The investigation reveals that the commonly used simpler version of the encoding is biased towards star networks, and that the initial population is dominated by only a few individuals. The more costly linkandnodebiased encoding uses not only a nodespecific bias, but also a linkspecific bias. Similarly to the nodebiased encoding, the linkandnodebiased encoding is also biased towards star networks, especially when using a low weighting for the linkspecific bias. The results show that by increasing the linkspecific bias, that the overall bias of the encoding is reduced. If researchers want to use the LNB encoding, and they are interested in having an unbiased representation, they should use higher values for the weight of the linkspecific bias. Nevertheless, they should also be aware of the limitations of the LNB encoding when using it for encoding tree problems. The encoding could be a good choice for the optimal communication spanning tree problem as the optimal solutions tend to be more starlike. However, for general tree problems the encoding should be used carefully.
Efficient Computation of Shortest Paths in Networks Using Particle Swarm Optimization and Noising Metaheuristics
, 2007
"... This paper presents a novel hybrid algorithm based on particle swarm optimization (PSO) and noising metaheuristics for solving the singlesource shortestpath problem (SPP) commonly encountered in graph theory. This hybrid search process combines PSO for iteratively finding a population of better so ..."
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Cited by 5 (0 self)
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This paper presents a novel hybrid algorithm based on particle swarm optimization (PSO) and noising metaheuristics for solving the singlesource shortestpath problem (SPP) commonly encountered in graph theory. This hybrid search process combines PSO for iteratively finding a population of better solutions and noising method for diversifying the search scheme to solve this problem. A new encoding/decoding scheme based on heuristics has been devised for representing the SPP parameters as a particle in PSO. Noisingmethodbased metaheuristics (noisy local search) have been incorporated in order to enhance the overall search efficiency. In particular, an iteration of the proposed hybrid algorithm consists of a standard PSO iteration and few trials of noising scheme applied to each better/improved particle for local search, where the neighborhood of each such particle is noisily explored with an elementary transformation of the particle so as to escape possible local minima and to diversify the search. Simulation results on several networks with random topologies are used to illustrate the efficiency of the proposed hybrid algorithm for shortestpath computation. The proposed algorithm can be used as a platform for solving other NPhard SPPs.