The Experts below are selected from a list of 359952 Experts worldwide ranked by ideXlab platform
Julien Gaffuri - One of the best experts on this subject based on the ideXlab platform.
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Designing generalisation Evaluation Function through human-machine dialogue
arXiv: Human-Computer Interaction, 2012Co-Authors: Patrick Taillandier, Julien GaffuriAbstract:Automated generalisation has known important improvements these last few years. However, an issue that still deserves more study concerns the automatic Evaluation of generalised data. Indeed, many automated generalisation systems require the utilisation of an Evaluation Function to automatically assess generalisation outcomes. In this paper, we propose a new approach dedicated to the design of such a Function. This approach allows an imperfectly defined Evaluation Function to be revised through a man-machine dialogue. The user gives its preferences to the system by comparing generalisation outcomes. Machine Learning techniques are then used to improve the Evaluation Function. An experiment carried out on buildings shows that our approach significantly improves generalisation Evaluation Functions defined by users.
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Using human-machine dialogue to refine generalisation Evaluation Function
2011Co-Authors: Patrick Taillandier, Julien GaffuriAbstract:Using human-machine dialogue to refine generalisation Evaluation Function. 25th International Cartographic Conference ICC 2011
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Using human-machine dialogue to refine generalisation Evaluation Function
2011Co-Authors: Patrick Taillandier, Julien GaffuriAbstract:More and more geographical data producers use automated generalisation to produce their data. Indeed, the spreading of artificial intelligence techniques has allowed an important improvement of the generalisation process automation. A classic approach consists in formalising generalisation as an optimisation problem: the goal is to find a state of the data that maximises a Function. All these methods use a Function that is supposed to assess the generalisation state of the data, according to the user need. We propose to call this Function "Evaluation Function" (the expressions "utility Function", "fitness Function", "energy" or even "satisfaction" are also often used). A key issue of this approach concerns the design of this Evaluation Function. Indeed, in order to get good results, such systems have to know what it is searching, i.e. what a good generalisation of the input data is. Unfortunately, designing such a Function remains a difficult task. Indeed, while the final user of the generalised data can easily describe his need in natural language, it is often far more difficult for him to express his expectations in a formal language that can be used by generalisation systems. This problem is particularly complex when numerous measures are used to characterise the quality of a generalisation and when no simple links between these measures values and the solution quality can be found. In this paper, we propose an approach dedicated to the design of generalisation Evaluation Functions. An Evaluation Function previously designed by a user is improved through a dialogue between this user and the generalisation system. The idea is to collect user preferences by letting the user compare different generalisation results for a same object (or group of objects).
Patrick Taillandier - One of the best experts on this subject based on the ideXlab platform.
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Designing generalisation Evaluation Function through human-machine dialogue
arXiv: Human-Computer Interaction, 2012Co-Authors: Patrick Taillandier, Julien GaffuriAbstract:Automated generalisation has known important improvements these last few years. However, an issue that still deserves more study concerns the automatic Evaluation of generalised data. Indeed, many automated generalisation systems require the utilisation of an Evaluation Function to automatically assess generalisation outcomes. In this paper, we propose a new approach dedicated to the design of such a Function. This approach allows an imperfectly defined Evaluation Function to be revised through a man-machine dialogue. The user gives its preferences to the system by comparing generalisation outcomes. Machine Learning techniques are then used to improve the Evaluation Function. An experiment carried out on buildings shows that our approach significantly improves generalisation Evaluation Functions defined by users.
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Using human-machine dialogue to refine generalisation Evaluation Function
2011Co-Authors: Patrick Taillandier, Julien GaffuriAbstract:Using human-machine dialogue to refine generalisation Evaluation Function. 25th International Cartographic Conference ICC 2011
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Using human-machine dialogue to refine generalisation Evaluation Function
2011Co-Authors: Patrick Taillandier, Julien GaffuriAbstract:More and more geographical data producers use automated generalisation to produce their data. Indeed, the spreading of artificial intelligence techniques has allowed an important improvement of the generalisation process automation. A classic approach consists in formalising generalisation as an optimisation problem: the goal is to find a state of the data that maximises a Function. All these methods use a Function that is supposed to assess the generalisation state of the data, according to the user need. We propose to call this Function "Evaluation Function" (the expressions "utility Function", "fitness Function", "energy" or even "satisfaction" are also often used). A key issue of this approach concerns the design of this Evaluation Function. Indeed, in order to get good results, such systems have to know what it is searching, i.e. what a good generalisation of the input data is. Unfortunately, designing such a Function remains a difficult task. Indeed, while the final user of the generalised data can easily describe his need in natural language, it is often far more difficult for him to express his expectations in a formal language that can be used by generalisation systems. This problem is particularly complex when numerous measures are used to characterise the quality of a generalisation and when no simple links between these measures values and the solution quality can be found. In this paper, we propose an approach dedicated to the design of generalisation Evaluation Functions. An Evaluation Function previously designed by a user is improved through a dialogue between this user and the generalisation system. The idea is to collect user preferences by letting the user compare different generalisation results for a same object (or group of objects).
Wajdi Trabelsi - One of the best experts on this subject based on the ideXlab platform.
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Job-Shop Scheduling with Mixed Blocking Constraints between Operations
2015Co-Authors: Christophe Sauvey, Nathalie Sauer, Wajdi TrabelsiAbstract:This paper addresses a hybrid job shop problem with identical machines, where several blocking constraints can be taken into account in a same problem. A mathematical linear integer model is proposed to be able to solve optimally the problem with commercial software. Since this problem is NP-hard, we developed an Evaluation Function in order to be able to solve bigger problems with classical meta-heuristics, thanks to a blocking matrix, containing the blocking constraints encountered after each operation of each job. The benchmark problems are proposed at the end of this paper, and the obtained results validate as much the proposed method as the Evaluation Function quality and suitability.
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Hybrid job shop scheduling with mixed blocking constraints between operations
2015Co-Authors: Christophe Sauvey, Wajdi TrabelsiAbstract:This paper addresses a hybrid job shop problem with identical machines, where several blocking constraints can be taken into account in a same problem. A mathematical linear integer model is proposed to be able to solve optimally the problem with commercial software. Since this problem is NPhard, we developed an Evaluation Function in order to be able to solve bigger problems with classical meta-heuristics, thanks to a blocking matrix, containing the blocking constraints encountered after each operation of each job. The benchmark problems are proposed at the end of this paper, and the obtained results validate as much the proposed method as the Evaluation Function quality and suitability.
Jose Torres-jimenez - One of the best experts on this subject based on the ideXlab platform.
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A refined Evaluation Function for the MinLA problem
Lecture Notes in Computer Science, 2006Co-Authors: Eduardo Rodriguez-tello, Jin-kao Hao, Jose Torres-jimenezAbstract:This paper introduces a refined Evaluation Function, called Φ, for the Minimum Linear Arrangement problem (MinLA). Compared with the classical Evaluation Function (LA), Φ integrates additional information contained in an arrangement to distinguish arrangements with the same LA value. The main characteristics of Φ are analyzed and its practical usefulness is assessed within both a Steepest Descent (SD) algorithm and a Memetic Algorithm (MA). Experiments show that the use of Φ allows to boost the performance of SD and MA, leading to the improvement on some previous best known solutions.
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MICAI - A refined Evaluation Function for the MinLA problem
Lecture Notes in Computer Science, 2006Co-Authors: Eduardo Rodriguez-tello, Jin-kao Hao, Jose Torres-jimenezAbstract:This paper introduces a refined Evaluation Function, called Φ, for the Minimum Linear Arrangement problem (MinLA). Compared with the classical Evaluation Function (LA), Φ integrates additional information contained in an arrangement to distinguish arrangements with the same LA value. The main characteristics of Φ are analyzed and its practical usefulness is assessed within both a Steepest Descent (SD) algorithm and a Memetic Algorithm (MA). Experiments show that the use of Φ allows to boost the performance of SD and MA, leading to the improvement on some previous best known solutions.
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An improved Evaluation Function for the Bandwidth Minimization Problem
Lecture Notes in Computer Science, 2004Co-Authors: Eduardo Rodriguez-tello, Jin-kao Hao, Jose Torres-jimenezAbstract:This paper introduces a new Evaluation Function, called δ, for the Bandwidth Minimization Problem for Graphs (BMPG). Compared with the classical β Evaluation Function used, our δ Function is much more discriminating and leads to smoother landscapes. The main characteristics of δ are analyzed and its practical usefulness is assessed within a Simulated Annealing algorithm. Experiments show that thanks to the use of the δ Function, we are able to improve on some previous best results of a set of well-known benchmarks.
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PPSN - An Improved Evaluation Function for the Bandwidth Minimization Problem
Lecture Notes in Computer Science, 2004Co-Authors: Eduardo Rodriguez-tello, Jin-kao Hao, Jose Torres-jimenezAbstract:This paper introduces a new Evaluation Function, called δ , for the Bandwidth Minimization Problem for Graphs (BMPG). Compared with the classical β Evaluation Function used, our δ Function is much more discriminating and leads to smoother landscapes. The main characteristics of δ are analyzed and its practical usefulness is assessed within a Simulated Annealing algorithm. Experiments show that thanks to the use of the δ Function, we are able to improve on some previous best results of a set of well-known benchmarks.
Andrew W Moore - One of the best experts on this subject based on the ideXlab platform.
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learning Evaluation Functions to improve optimization by local search
Journal of Machine Learning Research, 2001Co-Authors: Justin A Boyan, Andrew W MooreAbstract:This paper describes algorithms that learn to improve search performance on large-scale optimization tasks. The main algorithm, STAGE, works by learning an Evaluation Function that predicts the outcome of a local search algorithm, such as hillclimbing or Walksat, from features of states visited during search. The learned Evaluation Function is then used to bias future search trajectories toward better optima on the same problem. Another algorithm, X-STAGE, transfers previously learned Evaluation Functions to new, similar optimization problems. Empirical results are provided on seven large-scale optimization domains: bin-packing, channel routing, Bayesian network structure-finding, radiotherapy treatment planning, cartogram design, Boolean satisfiability, and Boggle board setup.
