Merz, P. (2000). Memetic algorithms for combinatorial optimization problems: Fitness landscapes and e#ective search strategies. Doctoral dissertation, University of Siegen, Germany. Home/Search Document Details and Download
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Balance between Genetic Search and Local Search in.. - Ishibuchi, Yoshida.. (2002) (1 citation) (Correct)
....(i.e. x x f f f = D ) between the current solution x and its neighbor x instead of the objective value ) x f of x . In the case of TSPs, the complexity of the calculation of f D is ) 1 ( O while that of ) x f is ) n O where n is the number of cities (for details, see [28] [29]) For example, let us consider Fig. 1 where a new tour is generated by removing the edges (1, 2) and (6, 7) and adding the edges (1, 6) and (2, 7) The difference in the objective values between the two tours can be calculated from only those four edges. On the other hand, when a new tour is ....
P. Merz, "Memetic algorithms for combinatorial optimization problems: Fitness landscape and effective search strategy," Ph. D. Thesis, University of Siegen, December 2000.
Intelligent Process Control utilising Symbiotic.. - Conradie.. (2 citations) (Correct)
....refinements to these near optimal solutions could significantly accelerate arriving at an optimal solution. However, EA s are not suited to focusing local refinements in large combinatorial tasks. Genetic evolution may be augmented to facilitate local (neighbourhood) search via cultural evolution [5]. Analogous to genetic propagation, cultural transmission (i.e. bird song) is the evolutionary flow of information. However, there are significant differences between cultural and genetic evolution. In cultural evolution, improvements are seldom a result of copying errors or the exchange of ....
....genes. The individuals in cultural evolution are conscious entities that use one another s ideas in the search process, subject to cooperation and competition. Genetic evolution has no concern for individual genes, but focuses on improving the population by propagating effective gene combinations [5]. Mereeric algorithms (MA) are evolutionary algorithms that use cultural evolution for local search (LS) The local search is applied to solutions in each generation of the EA, creating a process of lifetime learning. The EA searches globally for regions containing significant optima, while the ....
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P. Merz, "Memetic algorithms for combinatorial optimization problems", Ph.D. thesis, University of Siegen, Germany, 2000.
Clustering Gene Expression Data with Memetic Algorithms.. - Speer, Merz, Spieth.. (2003) Self-citation (Merz) (Correct)
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P. Merz. Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies. PhD thesis, Department of Electrical Engineering and Computer Science, University of Siegen, Germany, 2000.
Memetic Algorithms for the Traveling Salesman Problem - Merz, Freisleben (1997) Self-citation (Merz) (Correct)
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P. Merz, Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and E ective Search Strategies. PhD thesis, (Department of Electrical Engineering and Computer Science, University of Siegen, Germany, 2000).
Clustering Gene Expression Data with Memetic Algorithms.. - Speer, Merz, Spieth.. (2003) Self-citation (Merz) (Correct)
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P. Merz. Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies. PhD thesis, Department of Electrical Engineering and Computer Science, University of Siegen, Germany, 2000.
A Comparison of Memetic Recombination Operators for the Traveling.. - Merz Self-citation (Merz) (Correct)
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P. Merz, Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and E ective Search Strategies. PhD thesis, Department of Electrical Engineering and Computer Science, University of Siegen, Germany, 2000.
Toward Truly "Memetic" Memetic Algorithms: discussion and.. - Krasnogor, Gustafson (2002) Self-citation (Algorithms Gustafson) (Correct)
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P. Merz. Memetic Algorithms for Combinatorial Optimization Problems:Fitness Landscapes and E ecitve Search Strategies. Ph.D. Thesis, Parallel Systems Research Group. Department of Electrical Engineering and Computer Science. University of Siegen., 2000. Krasnogor and Gustafson
Clustering Gene Expression Profiles with Memetic Algorithms - Merz, Zell (2002) (5 citations) Self-citation (Merz) (Correct)
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Merz, P.: Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and E ective Search Strategies. PhD thesis, Department of Electrical Engineering and Computer Science, University of Siegen, Germany (2000)
On the Performance of Memetic Algorithms in Combinatorial.. - Merz (2001) (2 citations) Self-citation (Merz) (Correct)
....and the evolutionary metasearch is performed by application of either a mutation or recombination operator to separate the e ects of mutation and recombination. To prevent premature convergence, a restart mechanism based on mutation is employed if the diversity of the population has gone lost [12]. The paper is organized as follows. In section 2, the eciency of local search and evolutionary meta search is discussed. The e ectiveness of memetic algorithms is investigated in section 3 utilizing tness landscape analysis techniques. Section 4 concludes the paper and outlines areas of future ....
....for the hardness of the instance for a local search. For the QAP and NK Landscapes, it can be observed that the number of iterations of a local search (the number of moves until a local optimum is reached) decreases for less correlated landscapes and the resulting solution quality becomes worse [16, 12, 9]. In most k opt local search algorithms such as the Lin Kernighan heuristic or the Kernighan Lin heuristic, moves from one solution to a neighboring solution consist of a variable number of sub moves which rely on a much simpler neighborhood, e.g. 2 opt 3 opt vs. k opt neighborhood in the TSP. ....
[Article contains additional citation context not shown here]
P. Merz, Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and E ective Search Strategies. PhD thesis, Department of Computer Science, University of Siegen, Germany, 2000.
Greedy and Local Search Heuristics for Unconstrained Binary.. - Merz, Freisleben (2000) Self-citation (Merz) (Correct)
.... of the heuristics in genetic algorithms appears to be a promising approach (Merz Freisleben, 1999b) Further studies have shown that the combination of evolutionary algorithms and k opt local are highly e ective applied to large problem instances (n 500) Katayama Narihisa, 2000; Merz, 2000). 18 There are several areas of future research. First, the implementation speci c parameter m of the k opt local search should be studied in more detail; an interesting question is whether there is an instance independent optimum value for the parameter or whether the parameter has to be tuned ....
Merz, P. (2000). Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and E ective Search Strategies. PhD thesis, Department of Electrical Engineering and Computer Science, University of Siegen, Germany. In preparation.
Substructural Neighborhoods for Local Search in the.. - Fernando Lobo Medal (Correct)
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Merz, P. (2000). Memetic algorithms for combinatorial optimization problems: Fitness landscapes and e#ective search strategies. Doctoral dissertation, University of Siegen, Germany.
Self Generating Metaheuristics in Bioinformatics: The Proteins.. - Krasnogor (2004) (Correct)
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P. Merz, "Memetic algorithms for combinatorial optimization problems: Fitness landscapes and effective search strategies," Ph.D. Thesis, Parallel Systems Research Group. Department of Electrical Engineering and Computer Science. University of Siegen, 2000.
A Memetic Co-Clustering Algorithm for Gene Expression.. - Speer, Spieth, Zell (2004) (Correct)
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P. Merz. Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies. PhD thesis, Department of Electrical Engineering and Computer Science, University of Siegen, Germany, 2000.
A Memetic Clustering Algorithm for the Functional Partition .. - Speer, Spieth, Zell (2004) (Correct)
No context found.
P. Merz. Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies. PhD thesis, Department of Electrical Engineering and Computer Science, University of Siegen, Germany, 2000.
A Memetic Co-Clustering Algorithm for Gene Expression.. - Speer, Spieth, Zell (2004) (Correct)
No context found.
P. Merz. Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies. PhD thesis, Department of Electrical Engineering and Computer Science, University of Siegen, Germany, 2000.
Self Generating Metaheuristics in Bioinformatics: The Proteins.. - Krasnogor (2004) (Correct)
No context found.
P. Merz, "Memetic algorithms for combinatorial optimization problems: Fitness landscapes and effective search strategies," Ph.D. Thesis, Parallel Systems Research Group. Department of Electrical Engineering and Computer Science. University of Siegen, 2000.
Competent Memetic Algorithms: Model, Taxonomy and Dessing Issues - Krasnogor, Smith (Correct)
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P. Merz, Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effecitve Search Strategies. Ph.D. Thesis, Parallel Systems Research Group. Department of Electrical Engineering and Computer Science. University of Siegen., 2000.
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