Grammar-based generation of variable-selection heuristics for constraint satisfaction problems

We propose a grammar-based genetic programming framework that generates variable-selection heuristics for solving constraint satisfaction problems. This approach can be considered as a generation hyper-heuristic. A grammar to express heuristics is extracted from successful human-designed variable-selection heuristics. The search is performed on the derivation sequences of this grammar using a strongly typed genetic programming framework. The approach brings two innovations to grammar-based hyper-heuristics in this domain: the incorporation of if-then-else rules to the function set, and the implementation of overloaded functions capable of handling different input dimensionality. Moreover, the heuristic search space is explored using not only evolutionary search, but also two alternative simpler strategies, namely, iterated local search and parallel hill climbing. We tested our approach on synthetic and real-world instances. The newly generated heuristics have an improved performance when compared against human-designed heuristics. Our results suggest that the constrained search space imposed by the proposed grammar is the main factor in the generation of good heuristics. However, to generate more general heuristics, the composition of the training set and the search methodology played an important role. We found that increasing the variability of the training set improved the generality of the evolved heuristics, and the evolutionary search strategy produced slightly better results

Towards a generalised metaheuristic model for continuous optimisation problems

Metaheuristics have become a widely used approach for solving a variety of practical problems. The literature is full of diverse metaheuristics based on outstanding ideas and with proven excellent capabilities. Nonetheless, oftentimes metaheuristics claim novelty when they are just recombining elements from other methods. Hence, the need for a standard metaheuristic model is vital to stop the current frenetic tendency of proposing methods chiefly based on their inspirational source. This work introduces a first step to a generalised and mathematically formal metaheuristic model, which can be used for studying and improving them. This model is based on a scheme of simple heuristics, which perform as building blocks that can be modified depending on the application. For this purpose, we define and detail all components and concepts of a metaheuristic (i.e., its search operators), such as heuristics. Furthermore, we also provide some ideas to take into account for exploring other search operator configurations in the future. To illustrate the proposed model, we analyse search operators from four well-known metaheuristics employed in continuous optimisation problems as a proof-of-concept. From them, we derive 20 different approaches and use them for solving some benchmark functions with different landscapes. Data show the remarkable capability of our methodology for building metaheuristics and detecting which operator to choose depending on the problem to solve. Moreover, we outline and discuss several future extensions of this model to various problem and solver domains.The Research Group in Intelligent Systems at the Tecnológico de Monterrey (México), the Project TEC-Chinese Academy of Sciences, and by the CONACyT Basic Science Project.http://www.mdpi.com/journal/mathematicsam2021Computer Scienc

Combinations of GAs and CSP Strategies for Solving Examination Timetabling Problems

This thesis investigates various combinations of Constraint Satisfaction Strategies with Genetic Algorithms (GA) for Solving Examination timetabling Problems (ETTPs). Since Timetabling Problems (TTPs) in their simplest form can be mapped onto GraphColouring Problems (GCP), strategies for solving these problems are also included in the research. The main GA-related issues addressed in this thesis involve the fitness function, the crossover operator and the representation. The primary contributions this investigation presents can be summarised as follows: (1) in relation to the fitness function, the Hardness Theory (HT) which intends to measure how hard it is to solve a Constraint Satisfaction Problem (CSP) has been applied with the aim of improving the quality of solutions produced by the GA with the standard penalty function which has been criticised for exhibiting a series of defects. The key idea is that the fitness value for each individual in the population at a given generation, is the measure of difficulty of solving the remaining unsolved problem, consisting of the events yet to be scheduled and the edges that connect them. Despite the fac

Approximating Multi-Objective Hyper-Heuristics for Solving 2D Irregular Cutting Stock Problems

This article presents a method based on the multi-objective evolutionary algorithm NSGA-II to approximate hyper-heuristics for solving irregular 2D cutting stock problems under multiple objectives. In this case, additionally to the traditional objective of minimizing the number of sheets used to fit a finite number of irregular pieces, the time required to perform the placement task is also minimized, leading to a bi-objective minimization problem with a tradeoff between the number of sheets and the time required for placing all pieces. We solve this problem using multi-objective hyper-heuristics (MOHHs), whose main idea consists of finding a set of simple heuristics which can be combined to find a general solution for a wide range of problems, where a single heuristic is applied depending on the current condition of the problem, instead of applying a unique single heuristic during the whole placement process. The MOHHs are approximated after going through a learning process by mean of the NSGA-II, which evolves combinations of condition-action rules producing at the end a set of Pareto-optimal MOHHs. We tested the approximated MMOHHs on several sets of benchmark problems, having outstanding results for most of the cases.status: publishe

Improving Deep Interactive Evolution with a Style-Based Generator for Artistic Expression and Creative Exploration

Deep interactive evolution (DeepIE) combines the capacity of interactive evolutionary computation (IEC) to capture a user’s preference with the domain-specific robustness of a trained generative adversarial network (GAN) generator, allowing the user to control the GAN output through evolutionary exploration of the latent space. However, the traditional GAN latent space presents feature entanglement, which limits the practicability of possible applications of DeepIE. In this paper, we implement DeepIE within a style-based generator from a StyleGAN model trained on the WikiArt dataset and propose StyleIE, a variation of DeepIE that takes advantage of the secondary disentangled latent space in the style-based generator. We performed two AB/BA crossover user tests that compared the performance of DeepIE against StyleIE for art generation. Self-rated evaluations of the performance were collected through a questionnaire. Findings from the tests suggest that StyleIE and DeepIE perform equally in tasks with open-ended goals with relaxed constraints, but StyleIE performs better in close-ended and more constrained tasks

A unified hyper-heuristic framework for solving bin packing problems

One- and two-dimensional packing and cutting problems occur in many commercial contexts, and it is often important to be able to get good-quality solutions quickly. Fairly simple deterministic heuristics are often used for this purpose, but such heuristics typically find excellent solutions for some problems and only mediocre ones for others. Trying several different heuristics on a problem adds to the cost. This paper describes a hyper-heuristic methodology that can generate a fast, deterministic algorithm capable of producing results comparable to that of using the best problem-specific heuristic, and sometimes even better, but without the cost of trying all the heuristics. The generated algorithm handles both one- and two-dimensional problems, including two-dimensional problems that involve irregular concave polygons. The approach is validated using a large set of 1417 such problems, including a new benchmark set of 480 problems that include concave polygons

Deep learning and knowledge graph for image/video captioning: A review of datasets, evaluation metrics, and methods

Abstract Generating an image/video caption has always been a fundamental problem of Artificial Intelligence, which is usually performed using the potential of Deep Learning Methods, Computer Vision, Knowledge Graphs, and Natural Language Processing (NLP). The significant task of image/video captioning is to describe visual content in terms of natural language. Due to a semantic gap, this presents a massive problem in understanding and explaining images or videos syntactically and semantically. The current systems need somewhere to fill the gap between low‐level and high‐level features while mapping. Therefore, to tackle this problem, there is a need to describe the latest research and methods to overcome difficulties and to propose effective solutions. This work thoroughly analyses and investigates the most related methods (deep learning and knowledge graph‐based approaches), benchmark datasets, and evaluation metrics with their benefits and limitations. Here we have also reviewed the state‐of‐the‐art methods related to image/video captioning and their applications in the current scenario. Finally, we provide thorough information on existing research with comparisons of results on benchmark datasets. We have also mentioned the existing challenges and future direction of research

Understanding the structure of bin packing problems through principal component analysis

This paper uses a knowledge discovery method, Principal Component Analysis (PCA), to gain a deeper understanding of the structure of bin packing problems and how this relates to the performance of heuristic approaches to solve them. The study considers six heuristics and their combination through an evolutionary hyper-heuristic framework. A wide set of problem instances is considered, including one-dimensional and two-dimensional regular and irregular problems. A number of problem features are considered, which are reduced to the subset of nine features that more strongly relate with heuristic performance. PCA is used to further reduce the dimensionality of the instance features and produce 2D maps. The performance of the heuristics and hyper-heuristics is then super-imposed into these maps to visually reveal relationships between problem features and heuristic behavior. Our analysis indicates that some instances are clearly harder to solve than others for all the studied heuristics and hyper-heuristics. The PCA maps give a valuable indication of the combination of features characterizing easy and hard to solve instances. We found indeed correlations between instance features and heuristic performance. The so-called DJD heuristics are able to best solve a large proportion of instances, but simpler and faster heuristics can outperform them in some cases. In particular when solving 1D instances with low number of pieces, and, more surprisingly, when solving some difficult 2D instances with small areas with low variability. This analysis can be generalized to other problem domains where a set of features characterize instances and several problem solving heuristics are available