4 results on '"Palma, Wenceslao"'
Search Results
2. Choice functions for Autonomous Search in Constraint Programming: GA vs PSO
- Author
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Soto, Ricardo, Crawford, Broderick, Misra, S., Palma, Wenceslao, Monfroy, Eric, Castro, Carlos, Paredes, Fernando, Escuela de Ingenieria Informatica [Valparaíso], Pontificia Universidad Católica de Valparaíso (PUCV), Laboratoire d'Informatique de Nantes Atlantique (LINA), Centre National de la Recherche Scientifique (CNRS)-Mines Nantes (Mines Nantes)-Université de Nantes (UN), Theory, Algorithms and Systems for Constraints (TASC), Centre National de la Recherche Scientifique (CNRS)-Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Département informatique - EMN, Mines Nantes (Mines Nantes)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Departamento de Informatica [Valparaíso, Chile], Universidad Tecnica Federico Santa Maria [Valparaiso] (UTFSM), Universidad Diego Portales [Santiago] (UDP), Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS), and Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Mines Nantes (Mines Nantes)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Département informatique - EMN
- Subjects
ograničeno programiranje ,samostalno pretraživanje ,umjetna inteligencija ,Artificial Intelligence ,Autonomous Search ,Constraint Programming ,ComputingMilieux_MISCELLANEOUS ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] - Abstract
Heurističke metode nizanja vrijednosti i varijabli su ključni element u ograničenom programiranju. Poznate su kao strategija nabrajanja i mogu značajno utjecati na postupak rješavanja problema. Međutim, prilično je teško izabrati odgovarajući heuristički postupak jer je komplicirano predvidjeti njihovo ponašanje. U zadnje je vrijeme za tu svrhu predloženo samostalno (autonomno) pretraživanje. Ideja je da se strategije koje su se pokazale lošima tijekom postupka rješavanja dinamički zamijene onima koje više obećavaju. Ta se zamjena izvodi korištenjem funkcije izbora, koja u zadanom vremenu procijenjuje ponuđenu strategiju preko indikatora kvalitete. Važnu ulogu u tom procesu ima optimizator kojemu je cilj fino podešavanje funkcije izbora kako bi se garantirala precizna procjena strategija. U ovom radu evaluiramo karakteristike dviju jakih funkcija izbora: prvu podržava genetski algoritam, a drugu optimizator roja čestica. Dajemo interesantne rezultate i demonstriramo mogućnost korištenja tih metoda optimiziranja za samostalno pretraživanje u kontekstu ograničenog programiranja., The variable and value ordering heuristics are a key element in Constraint Programming. Known together as the enumeration strategy they may have important consequences on the solving process. However, a suitable selection of heuristics is quite hard as their behaviour is complicated to predict. Autonomous search has been recently proposed to handle this concern. The idea is to dynamically replace strategies that exhibit poor performances by more promising ones during the solving process. This replacement is carried out by a choice function, which evaluates a given strategy in a given amount of time via quality indicators. An important phase of this process is performed by an optimizer, which aims at finely tuning the choice function in order to guarantee a precise evaluation of strategies. In this paper we evaluate the performance of two powerful choice functions: the first one supported by a genetic algorithm and the second one by a particle swarm optimizer. We present interesting results and we demonstrate the feasibility of using those optimization techniques for Autonomous Search in a Constraint Programming context.
- Published
- 2013
3. Top-k Based Adaptive Enumeration in Constraint Programming.
- Author
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Soto, Ricardo, Crawford, Broderick, Palma, Wenceslao, Monfroy, Eric, Olivares, Rodrigo, Castro, Carlos, and Paredes, Fernando
- Subjects
CONSTRAINT programming ,MATHEMATICAL optimization ,MATHEMATICAL variables ,ALGORITHMS ,MATHEMATICAL analysis ,MATHEMATICAL models - Abstract
Constraint programming effectively solves constraint satisfaction and optimization problems by basically building, pruning, and exploring a search tree of potential solutions. In this context, a main component is the enumeration strategy, which is responsible for selecting the order in which variables and values are selected to build a possible solution. This process is known to be quite important; indeed a correct selection can reach a solution without failed explorations. However, it is well known that selecting the right strategy is quite challenging as their performance is notably hard to predict. During the last years, adaptive enumeration appeared as a proper solution to this problem. Adaptive enumeration allows the solving algorithm being able to autonomously modifying its strategies in solving time depending on performance information. In this way, the most suitable order for variables and values is employed along the search. In this paper, we present a new and more lightweight approach for performing adaptive enumeration. We incorporate a powerful classification technique named Top-k in order to adaptively select strategies along the resolution. We report results on a set of well-known benchmarks where the proposed approach noticeably competes with classical and modern adaptive enumeration methods for constraint satisfaction. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
4. A Hybrid Soft Computing Approach for Subset Problems.
- Author
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Crawford, Broderick, Soto, Ricardo, Monfroy, Eric, Castro, Carlos, Palma, Wenceslao, and Paredes, Fernando
- Subjects
SOFT computing ,SET theory ,MATHEMATICAL optimization ,ANT algorithms ,PROBLEM solving ,METAHEURISTIC algorithms ,SWARM intelligence ,CONSTRAINT programming - Abstract
Subset problems (set partitioning, packing, and covering) are formal models for many practical optimization problems. A set partitioning problem determines how the items in one set (S) can be partitioned into smaller subsets. All items in S must be contained in one and only one partition. Related problems are set packing (all itemsmust be contained in zero or one partitions) and set covering (all itemsmust be contained in at least one partition).Here, we present a hybrid solver based on ant colony optimization (ACO) combined with arc consistency for solving this kind of problems. ACO is a swarm intelligence metaheuristic inspired on ants behavior when they search for food. It allows to solve complex combinatorial problems for which traditional mathematical techniques may fail. By other side, in constraint programming, the solving process of Constraint Satisfaction Problems can dramatically reduce the search space by means of arc consistency enforcing constraint consistencies either prior to or during search. Our hybrid approach was tested with set covering and set partitioning dataset benchmarks. It was observed that the performance of ACO had been improved embedding this filtering technique in its constructive phase. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
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