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Accessed from Metaheuristics and Combinatorial Optimization Problems
, 2006
"... Metaheuristics and combinatorial optimization problems ..."
On Solving Combinatorial Optimization Problems
, 2002
"... We present a new viewpoint on how some combinatorial optimization problems are solved. When applying this viewpoint to the NPequivalent traveling salesman problem (TSP), we naturally arrive to a conjecture that is closely related to the polynomialtime insolvability of TSP, and hence to the P −NP ..."
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We present a new viewpoint on how some combinatorial optimization problems are solved. When applying this viewpoint to the NPequivalent traveling salesman problem (TSP), we naturally arrive to a conjecture that is closely related to the polynomialtime insolvability of TSP, and hence to the P −NP
Metaheuristics and Combinatorial Optimization Problems
"... The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. iii Scriptor, Gerald Skidmore (M.S., C.S.) Metaheuristics and Combinatorial Optimization Prob ..."
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The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. iii Scriptor, Gerald Skidmore (M.S., C.S.) Metaheuristics and Combinatorial Optimization
Minimum entropy combinatorial optimization problems
 Proceedings of the 5th Conference on Computability in Europe (CiE 2009
, 2009
"... We survey recent results on combinatorial optimization problems in which the objective function is the entropy of a discrete distribution. These include the minimum entropy set cover, minimum entropy orientation, and minimum entropy coloring problems. 1 ..."
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Cited by 4 (0 self)
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We survey recent results on combinatorial optimization problems in which the objective function is the entropy of a discrete distribution. These include the minimum entropy set cover, minimum entropy orientation, and minimum entropy coloring problems. 1
An Evolutionary Approach to Combinatorial Optimization Problems
 PROCEEDINGS OF THE 22ND ANNUAL ACM COMPUTER SCIENCE CONFERENCE
, 1994
"... The paper reports on the application of genetic algorithms, probabilistic search algorithms based on the model of organic evolution, to NPcomplete combinatorial optimization problems. In particular, the subset sum, maximum cut, and minimum tardy task problems are considered. Except for the fitness ..."
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Cited by 45 (5 self)
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The paper reports on the application of genetic algorithms, probabilistic search algorithms based on the model of organic evolution, to NPcomplete combinatorial optimization problems. In particular, the subset sum, maximum cut, and minimum tardy task problems are considered. Except for the fitness
Combinatorial optimization problems in selfassembly
 In Proceedings of the thiryfourth annual ACM symposium on Theory of computing
, 2002
"... Selfassembly is the ubiquitous process by which simple objects autonomously assemble into intricate complexes. It has been suggested that intricate selfassembly processes will ultimately be used in circuit fabrication, nanorobotics, DNA computation, and amorphous computing. In this paper, we stud ..."
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Cited by 43 (4 self)
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study two combinatorial optimization problems related to efficient selfassembly of shapes in the Tile Assembly Model of selfassembly proposed by Rothemund and Winfree [18]. The first is the Minimum Tile Set Problem, where the goal is to find the smallest tile system that uniquely produces a given
Metaheuristics for Dynamic Combinatorial Optimization Problems
"... Many realworld optimization problems are combinatorial optimization problems subject to dynamic environments. In such dynamic combinatorial optimization problems (DCOPs), the objective, decision variables, and/or constraints may change over time, and so solving DCOPs is a challenging task. Metaheu ..."
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Many realworld optimization problems are combinatorial optimization problems subject to dynamic environments. In such dynamic combinatorial optimization problems (DCOPs), the objective, decision variables, and/or constraints may change over time, and so solving DCOPs is a challenging task
Approximation thresholds for combinatorial optimization problems
 In Proceedings of the International Congress of Mathematicians
, 2002
"... An NPhard combinatorial optimization problem Π is said to have an approximation threshold if there is some t such that the optimal value of Π can be approximated in polynomial time within a ratio of t, and it is NPhard to approximate it within a ratio better than t. We survey some of the known app ..."
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An NPhard combinatorial optimization problem Π is said to have an approximation threshold if there is some t such that the optimal value of Π can be approximated in polynomial time within a ratio of t, and it is NPhard to approximate it within a ratio better than t. We survey some of the known
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124,524