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TSP heuristics with large domination number
, 1998
"... This paper is motivated by the following question of Glover and Punnen [5]: does there exist polynomial time (in n) algorithm A for the traveling salesman problem (TSP) on n cities such that dom(A) n!=p(n) for some polynomial p(n). ..."
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Cited by 6 (4 self)
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This paper is motivated by the following question of Glover and Punnen [5]: does there exist polynomial time (in n) algorithm A for the traveling salesman problem (TSP) on n cities such that dom(A) n!=p(n) for some polynomial p(n).
TSP Heuristics: Domination Analysis and Complexity
, 2001
"... We show that the 2Opt and 3Opt heuristics for the traveling salesman problem (TSP) on a complete graph K n produce a solution no worse than the average cost of a tour in K n in a polynomial number of iterations. As a consequence, the domination numbers of the 2 Opt and 3 Opt, CarlierVillon, Short ..."
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Cited by 6 (1 self)
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We show that the 2Opt and 3Opt heuristics for the traveling salesman problem (TSP) on a complete graph K n produce a solution no worse than the average cost of a tour in K n in a polynomial number of iterations. As a consequence, the domination numbers of the 2 Opt and 3 Opt, Carlier
Evolving {TSP} Heuristics Using Multi Expression Programming
 In Computational Science  ICCS 2004: 4th Intl. Conf., Part II
, 2004
"... Abstract. Multi Expression Programming (MEP) is an evolutionary technique that may be used for solving computationally difficult problems. MEP uses a linear solution representation. Each MEP individual is a string encoding complex expressions (computer programs). A MEP individual may encode multiple ..."
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Cited by 9 (1 self)
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multiple solutions of the current problem. In this paper MEP is used for evolving a Traveling Salesman Problem (TSP) heuristic for graphs satisfying triangle inequality. Evolved MEP heuristic is compared with Nearest Neighbor Heuristic (NN) and Minimum Spanning Tree Heuristic (MST) on some difficult
On the Integration of a TSP Heuristic into an EA for the Biobjective Ring Star Problem
 in "International Workshop on Hybrid Metaheuristics (HM 2008), Malaga, Spain", Lecture Notes in Computer Science
"... Abstract. This paper discusses a new hybrid solution method for a biobjective routing problem, namely the biobjective ring star problem. The biobjective ring star problem is a generalization of the ring star problem in which the assignment cost has been dissociated from the cost of visiting a sub ..."
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Cited by 3 (1 self)
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subset of nodes. Here, we investigate the possible contribution of incorporating specialized TSP heuristics into a multiobjective evolutionary algorithm. Experiments show that the use of this hybridization scheme allows a strict improvement of the generated sets of nondominated solutions. 1
SetAdditive and TSP Heuristics for planning with action costs and soft goals
, 2007
"... We introduce a nonadmissible heuristic for planning with action costs, called the setadditive heuristic, that combines the benefits of the additive heuristic used in the HSP planner and the relaxed plan heuristic used in FF. The setadditive heuristic is defined mathematically and handles nonunif ..."
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Cited by 7 (3 self)
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We introduce a nonadmissible heuristic for planning with action costs, called the setadditive heuristic, that combines the benefits of the additive heuristic used in the HSP planner and the relaxed plan heuristic used in FF. The setadditive heuristic is defined mathematically and handles non
The FF planning system: Fast plan generation through heuristic search
 Journal of Artificial Intelligence Research
, 2001
"... We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be ind ..."
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Cited by 822 (53 self)
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We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts
Planning as Heuristic Search
 Artificial Intelligence
, 2001
"... In the AIPS98 Planning Contest, the hsp planner showed that heuristic search planners can be competitive with state of the art Graphplan and sat planners. Heuristic search planners like hsp transform planning problems into problems of heuristic search by automatically extracting heuristics from S ..."
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Cited by 423 (34 self)
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In the AIPS98 Planning Contest, the hsp planner showed that heuristic search planners can be competitive with state of the art Graphplan and sat planners. Heuristic search planners like hsp transform planning problems into problems of heuristic search by automatically extracting heuristics from
Polynomial time approximation schemes for Euclidean TSP and other geometric problems
 In Proceedings of the 37th IEEE Symposium on Foundations of Computer Science (FOCS’96
, 1996
"... Abstract. We present a polynomial time approximation scheme for Euclidean TSP in fixed dimensions. For every fixed c � 1 and given any n nodes in � 2, a randomized version of the scheme finds a (1 � 1/c)approximation to the optimum traveling salesman tour in O(n(log n) O(c) ) time. When the nodes a ..."
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Cited by 399 (3 self)
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Abstract. We present a polynomial time approximation scheme for Euclidean TSP in fixed dimensions. For every fixed c � 1 and given any n nodes in � 2, a randomized version of the scheme finds a (1 � 1/c)approximation to the optimum traveling salesman tour in O(n(log n) O(c) ) time. When the nodes
The Ant System: Optimization by a colony of cooperating agents
 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICSPART B
, 1996
"... An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call Ant System. We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation ..."
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Cited by 1241 (45 self)
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computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed
The ant colony optimization metaheuristic
 in New Ideas in Optimization
, 1999
"... Ant algorithms are multiagent systems in which the behavior of each single agent, called artificial ant or ant for short in the following, is inspired by the behavior of real ants. Ant algorithms are one of the most successful examples of swarm intelligent systems [3], and have been applied to many ..."
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Cited by 385 (23 self)
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Ant algorithms are multiagent systems in which the behavior of each single agent, called artificial ant or ant for short in the following, is inspired by the behavior of real ants. Ant algorithms are one of the most successful examples of swarm intelligent systems [3], and have been applied to many types of problems, ranging from the classical traveling salesman
Results 1  10
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