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Modern continuous optimization algorithms for tuning real and integer algorithm parameters
 LNCS 6234. Proceedings of the International Conference on Swarm Intelligence (ANTS 2010
, 2010
"... Abstract. To obtain peak performance from optimization algorithms, it is required to set appropriately their parameters. Frequently, algorithm parameters can take values from the set of real numbers, or from a large integer set. To tune this kind of parameters, it is interesting to apply stateofth ..."
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Cited by 11 (3 self)
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Abstract. To obtain peak performance from optimization algorithms, it is required to set appropriately their parameters. Frequently, algorithm parameters can take values from the set of real numbers, or from a large integer set. To tune this kind of parameters, it is interesting to apply stateoftheart continuous optimization algorithms instead of using a tedious, and errorprone, handson approach. In this paper, we study the performance of several continuous optimization algorithms for the algorithm parameter tuning task. As case studies, we use a number of optimization algorithms from the swarm intelligence literature. 1
Hybrid metaheuristics for the vehicle routing problem with stochastic demands
 JOURNAL OF MATHEMATICAL
, 2006
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AutoMoDe: A novel approach to the automatic design of control software for robot swarms. Supplementary information page at http
 Neural Computation
, 2013
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Kernelization as heuristic structure for the vertex cover problem
 In: Proceedings of the Third Workshop on Ant Colony Optimization and Swarm Intelligence (ANTS 2006
, 2006
"... For solving combinatorial optimisation problems, exact methods accurately exploit the structure of the problem but are tractable only up to a certain size; approximation or heuristic methods are tractable for very large problems but may possibly be led into a bad solution. A question that arises is ..."
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For solving combinatorial optimisation problems, exact methods accurately exploit the structure of the problem but are tractable only up to a certain size; approximation or heuristic methods are tractable for very large problems but may possibly be led into a bad solution. A question that arises is, From where can we obtain knowledge of the problem structure via exact methods that can be exploited on largescale problems by heuristic methods? We present a framework that allows the exploitation of existing techniques and resources to integrate such structural knowledge into the Ant Colony System metaheuristic, where the structure is determined through the notion of kernelization from the field of parameterized complexity. We give experimental results using vertex cover as the problem instance, and show that knowledge of this type of structure improves performance beyond previously defined ACS algorithms.
Statistical methods for the comparison of stochastic optimizers
 MIC2005. THE 6TH METAHEURISTICS INTERNATIONAL CONFERENCE
, 2005
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Mixed Models for the Analysis of Local Search Components
 IN ENGINEERING STOCHASTIC LOCAL SEARCH ALGORITHMS. DESIGNING, IMPLEMENTING AND ANALYZING EFFECTIVE HEURISTICS
, 2007
"... We consider a possible scenario of experimental analysis on heuristics for optimization: identifying the contribution of local search components when algorithms are evaluated on the basis of solution quality attained. We discuss the experimental designs with special focus on the role of the test ins ..."
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Cited by 3 (2 self)
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We consider a possible scenario of experimental analysis on heuristics for optimization: identifying the contribution of local search components when algorithms are evaluated on the basis of solution quality attained. We discuss the experimental designs with special focus on the role of the test instances in the statistical analysis. Contrary to previous practice of modeling instances as a blocking factor, we treat them as a random factor. Together with algorithms, or their components, which are fixed factors, this leads naturally to a mixed ANOVA model. We motivate our choice and illustrate the application of the mixed model on a study of local search for the 2edgeconnectivity problem.
Exactness as Heuristic Structure for Guiding Ant Colony System
, 2006
"... For solving combinatorial optimisation problems, exact methods accurately exploit the structure of the problem but are tractable only up to a certain size; approximation or heuristic methods are tractable for very large problems but may possibly be led into a bad solution. A question that arises is, ..."
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Cited by 1 (1 self)
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For solving combinatorial optimisation problems, exact methods accurately exploit the structure of the problem but are tractable only up to a certain size; approximation or heuristic methods are tractable for very large problems but may possibly be led into a bad solution. A question that arises is, From where can we obtain knowledge of the problem structure via exact methods that can be used on largescale problems by heuristic methods? We present a framework that allows the exploitation of existing techniques and resources to integrate such structural knowledge into the Ant Colony System metaheuristic, where the structure is determined through the notion of kernelization from the field of parameterized complexity. We give experimental results using vertex cover as the problem instance, and show that knowledge of this type of structure improves performance beyond previously defined ACS algorithms.