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José Carlos Ortiz-Bayliss

Other affiliations: University of Nottingham
Bio: José Carlos Ortiz-Bayliss is an academic researcher from Monterrey Institute of Technology and Higher Education. The author has contributed to research in topics: Heuristics & Constraint satisfaction problem. The author has an hindex of 10, co-authored 68 publications receiving 370 citations. Previous affiliations of José Carlos Ortiz-Bayliss include University of Nottingham.


Papers
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Book ChapterDOI
23 Jun 2021
TL;DR: In this paper, the authors explore the application of the language-image model, CLIP, to obtain video representations without the need for said annotations and obtain state-of-the-art results on the MSR-VTT and MSVD benchmarks.
Abstract: Video Retrieval is a challenging task where the task aims at matching a text query to a video or vice versa. Most of the existing approaches for addressing such a problem rely on annotations made by the users. Although simple, this approach is not always feasible in practice. In this work, we explore the application of the language-image model, CLIP, to obtain video representations without the need for said annotations. This model was explicitly trained to learn a common space where images and text can be compared. Using various techniques described in this document, we extended its application to videos, obtaining state-of-the-art results on the MSR-VTT and MSVD benchmarks.

64 citations

Journal ArticleDOI
TL;DR: The main contributions of this paper are the definition of two meta-feature sets, one based on what the authors call ’basic’ instance properties and another based on the number of feasible solutions found by perturbative heuristics via a greedy process.
Abstract: This paper describes a method for solving vehicle routing problems with time windows, based on selecting meta-heuristics via meta-learning. Although several meta-heuristics already exist that can obtain good overall results on some vehicle routing problem instances, none of them performs well in all cases. By defining a set of meta-features that appropriately characterize different routing problem instances and using a suitable classifier, our model can often correctly predict the best meta-heuristic for each instance. The main contributions of this paper are the definition of two meta-feature sets, one based on what we call ’basic’ instance properties and another based on the number of feasible solutions found by perturbative heuristics via a greedy process. We use a multilayer perceptron classifier, combined with a wrapper meta-feature selection method, to predict the most suitable meta-heuristic to apply to a given problem instance. Our experimental results show that the proposed method can significantly improve upon the overall performance of well-known meta-heuristics in the field. Therefore, this paper proposes to store, share and exploit in an off-line scheme the solutions obtained in instances of different scenarios such as the academy or industry, with the aim of predicting good solvers for new instances when necessary.

37 citations

Proceedings ArticleDOI
12 Jul 2008
TL;DR: A GA-based method that produces general hyper-heuristics for the dynamic variable ordering within Constraint Satisfaction Problems using a variable-length representation is presented.
Abstract: The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combination should outperform the single heuristics. This paper presents a GA-based method that produces general hyper-heuristics for the dynamic variable ordering within Constraint Satisfaction Problems. The GA uses a variable-length representation, which evolves combinations of condition-action rules producing hyper-heuristics after going through a learning process which includes training and testing phases. Such hyper-heuristics, when tested with a large set of benchmark problems, produce encouraging results for most of the cases. The testebed is composed of problems randomly generated using an algorithm proposed by Prosser.

35 citations

Journal ArticleDOI
TL;DR: This work begins by determining the most relevant combinatorial optimization problems, and then it analyzes them in the context of hyper-heuristics to verify whether they remain as relevant when considering exclusively works related to hyper- heuristics.
Abstract: Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution. The problem changes; so does its state. As a consequence, for the next move, a different solver may be invoked. Hyper-heuristics have been around for almost 20 years. However, combinatorial optimization problems date from way back. Thus, it is paramount to determine whether the efforts revolving around hyper-heuristic research have been targeted at the problems of the highest interest for the combinatorial optimization community. In this work, we tackle such an endeavor. We begin by determining the most relevant combinatorial optimization problems, and then we analyze them in the context of hyper-heuristics. The idea is to verify whether they remain as relevant when considering exclusively works related to hyper-heuristics. We find that some of the most relevant problem domains have also been popular for hyper-heuristics research. Alas, others have not and few efforts have been directed towards solving them. We identify the following problem domains, which may help in furthering the impact of hyper-heuristics: Shortest Path, Set Cover, Longest Path, and Minimum Spanning Tree. We believe that focusing research on ways for solving them may lead to an increase in the relevance and impact that hyper-heuristics have on combinatorial optimization problems.

32 citations

Proceedings ArticleDOI
10 Jun 2019
TL;DR: The classical stochastic local optimization algorithm Simulated Annealing is used to train a selection hyper-heuristic for solving JSSPs and the results suggest that training with the highest number of instances lead to better and more stable hyper- heuristics.
Abstract: Job Shop Scheduling problems (JSSPs) have become increasingly popular due to their application in supply chain systems. Several solution approaches have appeared in the literature. One of them is the use of low-level heuristics. These methods approximate a solution but only work well on some kind of problems. Hence, combining them may improve performance. In this paper, we use the classical stochastic local optimization algorithm Simulated Annealing to train a selection hyper-heuristic for solving JSSPs. To do so, we use an instance generator provided in literature to create training sets with a different number of instances: 20, 40, and 60. In addition, we select instances from the literature to create two test scenarios, one similar to the training instances, and another with bigger problems. Our results suggest that training with the highest number of instances lead to better and more stable hyper-heuristics. For example, in the first test scenario, we achieved a reduction in the data range of over 60% and an improvement in the median performance of almost 30%. Moreover, under these conditions about 75% of the generated hyper-heuristics were able to perform equal to or better than the best heuristic. Even so, less than 25% were able to outperform the synthetic Oracle. Because of the aforementioned, we strongly support the idea of using a selection hyper-heuristic model powered by Simulated Annealing for creating a high-level solver for Job Shop Scheduling problems.

29 citations


Cited by
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Journal ArticleDOI
TL;DR: A critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas are presented.
Abstract: Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.

1,023 citations

01 Jan 2007
TL;DR: Minimum Cardinality Matrix Decomposition into Consecutive-Ones Matrices: CP and IP Approaches and Connections in Networks: Hardness of Feasibility Versus Optimality.
Abstract: Minimum Cardinality Matrix Decomposition into Consecutive-Ones Matrices: CP and IP Approaches.- Connections in Networks: Hardness of Feasibility Versus Optimality.- Modeling the Regular Constraint with Integer Programming.- Hybrid Local Search for Constrained Financial Portfolio Selection Problems.- The "Not-Too-Heavy Spanning Tree" Constraint.- Eliminating Redundant Clauses in SAT Instances.- Cost-Bounded Binary Decision Diagrams for 0-1 Programming.- YIELDS: A Yet Improved Limited Discrepancy Search for CSPs.- A Global Constraint for Total Weighted Completion Time.- Computing Tight Time Windows for RCPSPWET with the Primal-Dual Method.- Necessary Condition for Path Partitioning Constraints.- A Constraint Programming Approach to the Hospitals / Residents Problem.- Best-First AND/OR Search for 0/1 Integer Programming.- A Position-Based Propagator for the Open-Shop Problem.- Directional Interchangeability for Enhancing CSP Solving.- A Continuous Multi-resources cumulative Constraint with Positive-Negative Resource Consumption-Production.- Replenishment Planning for Stochastic Inventory Systems with Shortage Cost.- Preprocessing Expression-Based Constraint Satisfaction Problems for Stochastic Local Search.- The Deviation Constraint.- The Linear Programming Polytope of Binary Constraint Problems with Bounded Tree-Width.- On Boolean Functions Encodable as a Single Linear Pseudo-Boolean Constraint.- Solving a Stochastic Queueing Control Problem with Constraint Programming.- Constrained Clustering Via Concavity Cuts.- Bender's Cuts Guided Large Neighborhood Search for the Traveling Umpire Problem.- A Large Neighborhood Search Heuristic for Graph Coloring.- Generalizations of the Global Cardinality Constraint for Hierarchical Resources.- A Column Generation Based Destructive Lower Bound for Resource Constrained Project Scheduling Problems.

497 citations

Book
01 Jan 1985
TL;DR: This work focuses on the application of Fuzzy and Artificial Intelligence Methods in the Building of a Blast Furnace Smelting Process Model and the development of Performance Adaptive FuzzY Controllers with Application to Continuous Casting Plants.
Abstract: Preface. Automatic Train Operation System by Predictive Fuzzy Control (S. Yasunobu, S. Miyamoto). Application of Fuzzy Reasoning to the Water Purification Process (O. Yagashita, O. Itoh, M. Sugeno). The Application of a Fuzzy Controller to the Control of a Multi-Degree-of-Freedom Robot Arm (E.M. Scharf, N.J. Mandic). Optimizing Control of a Diesel Engine (Y. Murayama et al.). Development of Performance Adaptive Fuzzy Controllers with Application to Continuous Casting Plants (G. Bartolini et al.). A Fuzzy Logic Controller for Aircraft Flight Control (L.I. Larkin). Automobile Speed Control System Using a Fuzzy Logic Controller (S. Murakami, M. Maeda). An Experimental Study on Fuzzy Parking Control Using a Model Car (M. Sugeno, K. Murakami). A Fuzzy Controller in Turning Process Automation (Y. Sakai, K. Ohkusa). Design of Fuzzy Control Algorithms with the Aid of Fuzzy Models (W. Pedrycz). Human Operator's Fuzzy Model in Man-Machine System with a Nonlinear Controlled Object (K. Matsushima, H. Sugiyama). The Influence of Some Parameters on the Accuracy of a Fuzzy Model (J.B. Kiszka, M.E. Kochanska, D.S. Sliwinska). A Microprocessor Based Fuzzy Controller for Industrial Purposes (T. Yamazaki, M. Sugeno). The Application of Fuzzy and Artificial Intelligence Methods in the Building of a Blast Furnace Smelting Process Model (H. Zhao, M. Ma). An Annotated Bibliography of Fuzzy Control (R.M. Tong).

439 citations

Journal ArticleDOI
04 May 2022
TL;DR: A minimalist design to pretrain an image-text encoder-decoder foundation model jointly with contrastive loss and captioning loss, thereby subsuming model capabilities from contrastive approaches like CLIP and generative methods like SimVLM.
Abstract: Exploring large-scale pretrained foundation models is of significant interest in computer vision because these models can be quickly transferred to many downstream tasks. This paper presents Contrastive Captioner (CoCa), a minimalist design to pretrain an image-text encoder-decoder foundation model jointly with contrastive loss and captioning loss, thereby subsuming model capabilities from contrastive approaches like CLIP and generative methods like SimVLM. In contrast to standard encoder-decoder transformers where all decoder layers attend to encoder outputs, CoCa omits cross-attention in the first half of decoder layers to encode unimodal text representations, and cascades the remaining decoder layers which cross-attend to the image encoder for multimodal image-text representations. We apply a contrastive loss between unimodal image and text embeddings, in addition to a captioning loss on the multimodal decoder outputs which predicts text tokens autoregressively. By sharing the same computational graph, the two training objectives are computed efficiently with minimal overhead. CoCa is pretrained end-to-end and from scratch on both web-scale alt-text data and annotated images by treating all labels simply as text, seamlessly unifying natural language supervision for representation learning. Empirically, CoCa achieves state-of-the-art performance with zero-shot transfer or minimal task-specific adaptation on a broad range of downstream tasks, spanning visual recognition (ImageNet, Kinetics-400/600/700, Moments-in-Time), crossmodal retrieval (MSCOCO, Flickr30K, MSR-VTT), multimodal understanding (VQA, SNLI-VE, NLVR2), and image captioning (MSCOCO, NoCaps). Notably on ImageNet classification, CoCa obtains 86.3% zero-shot top-1 accuracy, 90.6% with a frozen encoder and learned classification head, and new state-of-the-art 91.0% top-1 accuracy on ImageNet with a finetuned encoder.

399 citations

Journal ArticleDOI
TL;DR: This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection.
Abstract: It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronou...

304 citations