P. Merz and B. Freisleben, "Genetic Algorithms for Binary Quadratic Programming," in GECCO-1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... and problem specific PLSAs (rapid convergence toward local minima) For instance, in the field of evolutionary computation hybridization seems to be common for real world applications [GV99] and a lot of evolutionary algorithm local search method combinations can be found in the literature, e.g. [Dav91, IM96, RDSW99, MF99, ZTB99]. When employing PLSAs, however, the question is how to use the computational resources most efficiently, i.e. either one is interested in finding a solution of sufficient quality in minimum time or the goal is to generate a maximum quality solution within a specified time [GV99] Here, we ....

Peter Merz and Bernd Freisleben. Genetic algorithms for binary quadratic programming. In GECCO'99, volume 1, pages 417-- 424, San Francisco, CA, 1999. Morgan Kaufmann,.

.... and problem specific PLSAs (rapid convergence toward local minima) For instance, in the field of evolutionary computation hybridization seems to be common for real world applications [GV99] and a lot of evolutionary algorithm local search method combinations can be found in the literature, e.g. [Dav91, IM96, RDSW99, MF99, ZTB99]. When employing PLSAs, however, the question is how to use the computational resources most efficiently, i.e. either one is interested in finding a solution of sufficient quality in minimum time or the goal is to generate a maximum quality solution within a specified time [GV99] Here, we ....

Peter Merz and Bernd Freisleben. Genetic algorithms for binary quadratic programming. In GECCO'99, volume 1, pages 417-- 424, San Francisco, CA, 1999. Morgan Kaufmann,.

No context found.

P. Merz and B. Freisleben, "Genetic Algorithms for Binary Quadratic Programming," in GECCO-1999.

No context found.

P. Merz and B. Freisleben, \Genetic Algorithms for Binary Quadratic Programming," in GECCO1999.

....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 superior to others, but it is not shown why. In an attempt ....

P. Merz and B. Freisleben, \Genetic Algorithms for Binary Quadratic Programming," in GECCO1999: Proc. of the Genetic and Evolutionary Computation Conference, (W. Banzhaf et al., eds.), pp. 417-424, Morgan Kauman, 1999.

.... been proposed recently to nd solutions to large problem instances, including tabu search (Beasley, 1998; Glover et al. 1998b; Glover et al. 1998a) scatter search (Amini et al. 1999) simulated annealing (Beasley, 1998; Katayama Narihisa, 1999) and evolutionary algorithms (Lodi et al. 1998; Merz Freisleben, 1999b) In this paper, a greedy heuristic and two local search algorithms for the unconstrained binary quadratic programming problem (BQP) are presented. The greedy heuristic constructs a feasible solution in n steps where n denotes the problem size, i.e. the number of elements of a solution. In each ....

....solutions of all problems in this set, but no CPU times were reported. The heuristics proposed in this paper are not intended to be competitive with these approaches. Instead, they have been developed with the idea of building powerful meta heuristics called memetic algorithms (Moscato, 1999; Merz Freisleben, 1999a) evolutionary algorithms incoporating greedy heuristics and local search. 4 Conclusions In this paper, a randomized greedy heuristic and two local search algorithms based on the 1 opt and a k opt neighborhood for the BQP have been proposed. The runtime behavior and the average performance of ....

[Article contains additional citation context not shown here]

Merz, P. & Freisleben, B. (1999b). Genetic Algorithms for Binary Quadratic Programming. In W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, & R. E. Smith (Eds.), GECCO-1999: Proceedings of the Genetic and Evolutionary Computation Conference (pp. 417-424).: Morgan Kauman.

.... in [16, 17] is a special case of a memetic algorithm, which has been shown to be very effective for several combinatorial optimization problems, such as the traveling salesman problem (TSP) 30] the graph bi partitioning problem (GBP) 31, 36] NK Landscapes [32] and binary quadratic programming [34]. In contrast to hybrid evolutionary algorithms that use local refinement techniques as additional operators, MAs are designed to search in the space of locally optimal solutions instead of searching in the space of all candidate solutions. This is achieved by applying local search after each of ....

P. Merz and B. Freisleben, "Genetic Algorithms for Binary Quadratic Programming," in GECCO-1999: Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kauffman, 1999. to appear.

No context found.

P. Merz and B. Freisleben, "Genetic algorithms for binary quadratic programming," in Proceedings of the 1999.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC