Merz, P. and Freisleben, B. (2000), Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem, IEEE Transaction On Evolutionary Computation, 4:337-352, IEEE Press, NY, USA.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....for most problems and the value r depends on the distance metric. This limitation exists in this study, too. Another factor is the local optimization. When studied with local optimum solutions, TSP showed fairly high FDC values [7] studied with random solutions, it showed very low FDC values [20]. The gures in Table 3 do not imply any easiness of TSP. 6 Conclusions We proposed a hybrid genetic algorithm for the hexagonal tortoise problem. Using our hybrid genetic algorithm, we could obtain optimal solutions for the problems with up to 70 numbers. It is much smaller than the tractable ....

P. Merz and B. Freisleben. Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Transactions on Evolutionary Computation, 4:337-352, 2000.

....obtained by other heuristics and other implementations of the TS, given in Table 2 in which Re TS stands for Reactive TS [1] Ro TS for Robust TS [21] MMAS for Min Max Ant System and MA denotes memetic algorithm. The numerical values, except for TSQAP column, are reproduced from table iv of [15]. We remark that the results from [15] have been obtained in a platform unknown to us; they did 30 independent runs and xed before the hand the run time 3 while we did 10 independent runs with 100 iterations per run. More experimental results can be found in Appendix A.1. instance best known ....

....other implementations of the TS, given in Table 2 in which Re TS stands for Reactive TS [1] Ro TS for Robust TS [21] MMAS for Min Max Ant System and MA denotes memetic algorithm. The numerical values, except for TSQAP column, are reproduced from table iv of [15] We remark that the results from [15] have been obtained in a platform unknown to us; they did 30 independent runs and xed before the hand the run time 3 while we did 10 independent runs with 100 iterations per run. More experimental results can be found in Appendix A.1. instance best known Re TS Ro TS MMAS MA TSQAP gap gap ....

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P. Merz and B. Freisleben. Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem. IEEE Trans. on Evol. Comp., 2000. To appear.

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P. Merz and B. Freisleben, "Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem," IEEE Transactions on Evolutionary Computation, vol. 4, no. 4, pp. 337--352, 2000.

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P. Merz and B. Freisleben, \Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem," IEEE Transactions on Evolutionary Computation, 4 (4), (2000), 337-352.

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P. Merz and B. Freisleben, \Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem," IEEE Transactions on Evolutionary Computation, vol. 4, no. 4, pp. 337-352, 2000.

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Merz, P., Freisleben, B.: Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem. IEEE Transactions on Evolutionary Computation 4 (2000) 337-352

....properties strongly in uence the e ectiveness of the evolutionary meta search. 1 INTRODUCTION Memetic algorithms (MAs) have been shown to be very e ective for many combinatorial optimization problems, including the traveling salesman problem (TSP) 14] the quadratic assignment problem (QAP) [13, 16], the binary quadratic programming problem (BQP) 15, 19] and graph bipartitioning (GBP) 17] Besides memetic algorithms, many other modern heuristics have been proposed for combinatorial optimization problems. Most work is concerned with showing that the proposed heuristic is e ective and ....

....terminate the local search without actually checking all neighboring solutions: With the concept of don t look bits, only a small, dynamically changing subset of the candidates in the neighborhood is considered. This concept has been used in local search algorithms for the TSP [2, 8] and the QAP [13, 16]. Furthermore, sophisticated data structures to perform the most time consuming tasks during the local search have been proposed. The use of these data structures can improve the performance of a local search drastically for large instances without in uencing the solution quality. In the TSP, ....

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P. Merz and B. Freisleben, \Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem," IEEE Transactions on Evolutionary Computation, vol. 4, no. 4, pp. 337-352, 2000.

....contest on evolutionary optimization (1st ICEO) 3] The results presented in Table 3. 2 are even better than previously published [22] In case of the QAP, our approach works extremely well and appears to be superior to tabu search, ant colonies, simulated annealing and also scatter search [21, 25, 5]. For NK landscapes we have shown that genetic local search is superior to genetic algorithms and multi start local search [24] Recently, we have shown for the GBP that our memetic algorithm is superior to other hybrid evolutionary approaches, simulated annealing, and in almost all cases ....

P. Merz and B. Freisleben, "Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem," Tech. Rep. 99-02, University of Siegen, Germany, 1999.

....time to search for the best 2 opt move in our local search procedure, as it is done, for example, in tabu search. Compared to the previously proposed approach [29] the implementation for evaluating DeltaC has been improved considerably. A detailed description of our local search can be found in [33]. 3.3 The Recombination Operator A distance measure between solutions may help in defining effective genetic operators for a given problem. There are several possibilities for measuring distances between permutations. The distance measure used in our approach is as follows. Let 1 and 2 be ....

P. Merz and B. Freisleben, "Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem," Tech. Rep. 99-02, University of Siegen, Germany, 1999.

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Merz, P. and Freisleben, B. (2000), Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem, IEEE Transaction On Evolutionary Computation, 4:337-352, IEEE Press, NY, USA.

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Peter Merz and Bernd Freisleben, "Fitness landscape analysis and memetic algorithms for the quadratic assignment problem, " IEEE Transactions on Evolutionary Computation, vol. 4, no. 4, pp. 337--352, Nov. 2000.

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P. Merz and B. Freisleben. Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Transactions on Evolutionary Computation, 4(4):337--352, November 2000.

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