A methodology for determining an effective subset of heuristics in selection hyper-heuristics

We address the important step of determining an effective subset of heuristics in selection hyper-heuristics. Little attention has been devoted to this in the literature, and the decision is left at the discretion of the investigator. The performance of a hyper-heuristic depends on the quality and size of the heuristic pool. Using more than one heuristic is generally advantageous, however, an unnecessary large pool can decrease the performance of adaptive approaches. Our goal is to bring methodological rigour to this step. The proposed methodology uses non-parametric statistics and fitness landscape measurements from an available set of heuristics and benchmark instances, in order to produce a compact subset of effective heuristics for the underlying problem. We also propose a new iterated local search hyper-heuristic usingmulti-armed banditscoupled with a change detection mechanism. The methodology is tested on two real-world optimisation problems: course timetabling and vehicle routing. The proposed hyper-heuristic with a compact heuristic pool, outperforms state-of-the-art hyper-heuristics and competes with problem-specific methods in course timetabling, even producing new best-known solutions in 5 out of the 24 studied instances

Effective learning hyper-heuristics for the course timetabling problem

Course timetabling is an important and recurring administrative activity in most educational institutions. This article combines a general modeling methodology with effective learning hyper-heuristics to solve this problem. The proposed hyper-heuristics are based on an iterated local search procedure that autonomously combines a set of move operators. Two types of learning for operator selection are contrasted: a static (offline) approach, with a clear distinction between training and execution phases; and a dynamic approach that learns on the fly. The resulting algorithms are tested over the set of real-world instances collected by the first and second International Timetabling competitions. The dynamic scheme statistically outperforms the static counterpart, and produces competitive results when compared to the state-of-the-art, even producing a new best-known solution. Importantly, our study illustrates that algorithms with increased autonomy and generality can outperform human designed problem-specific algorithms

Iterated local search using an add and delete hyper- heuristic for university course timetabling

Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach

A Methodology for Classifying Search Operators as Intensification or Diversification Heuristics

Selection hyper-heuristics are generic search tools that dynamically choose, from a given pool, the most promising operator (low-level heuristic) to apply at each iteration of the search process. The performance of these methods depends on the quality of the heuristic pool. Two types of heuristics can be part of the pool: diversification heuristics, which help to escape from local optima, and intensification heuristics, which effectively exploit promising regions in the vicinity of good solutions. An effective search strategy needs a balance between these two strategies. However, it is not straightforward to categorize an operator as intensification or diversification heuristic on complex domains. Therefore, we propose an automated methodology to do this classification. This brings methodological rigor to the configuration of an iterated local search hyper-heuristic featuring diversification and intensification stages. The methodology considers the empirical ranking of the heuristics based on an estimation of their capacity to either diversify or intensify the search. We incorporate the proposed approach into a state-of-the-art hyper-heuristic solving two domains: course timetabling and vehicle routing. Our results indicate improved performance, including new best-known solutions for the course timetabling problem

Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence

In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI). Particle Swarm Optimisation (PSO) is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO) is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP); it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO

Temperature Effects Explain Continental Scale Distribution of Cyanobacterial Toxins

Insight into how environmental change determines the production and distribution of cyanobacterial toxins is necessary for risk assessment. Management guidelines currently focus on hepatotoxins (microcystins). Increasing attention is given to other classes, such as neurotoxins (e.g., anatoxin-a) and cytotoxins (e.g., cylindrospermopsin) due to their potency. Most studies examine the relationship between individual toxin variants and environmental factors, such as nutrients, temperature and light. In summer 2015, we collected samples across Europe to investigate the effect of nutrient and temperature gradients on the variability of toxin production at a continental scale. Direct and indirect effects of temperature were the main drivers of the spatial distribution in the toxins produced by the cyanobacterial community, the toxin concentrations and toxin quota. Generalized linear models showed that a Toxin Diversity Index (TDI) increased with latitude, while it decreased with water stability. Increases in TDI were explained through a significant increase in toxin variants such as MC-YR, anatoxin and cylindrospermopsin, accompanied by a decreasing presence of MC-LR. While global warming continues, the direct and indirect effects of increased lake temperatures will drive changes in the distribution of cyanobacterial toxins in Europe, potentially promoting selection of a few highly toxic species or strains.Peer reviewe

VIII Encuentro de Docentes e Investigadores en Historia del Diseño, la Arquitectura y la Ciudad

Acta de congresoLa conmemoración de los cien años de la Reforma Universitaria de 1918 se presentó como una ocasión propicia para debatir el rol de la historia, la teoría y la crítica en la formación y en la práctica profesional de diseñadores, arquitectos y urbanistas. En ese marco el VIII Encuentro de Docentes e Investigadores en Historia del Diseño, la Arquitectura y la Ciudad constituyó un espacio de intercambio y reflexión cuya realización ha sido posible gracias a la colaboración entre Facultades de Arquitectura, Urbanismo y Diseño de la Universidad Nacional y la Facultad de Arquitectura de la Universidad Católica de Córdoba, contando además con la activa participación de mayoría de las Facultades, Centros e Institutos de Historia de la Arquitectura del país y la región. Orientado en su convocatoria tanto a docentes como a estudiantes de Arquitectura y Diseño Industrial de todos los niveles de la FAUD-UNC promovió el debate de ideas a partir de experiencias concretas en instancias tales como mesas temáticas de carácter interdisciplinario, que adoptaron la modalidad de presentación de ponencias, entre otras actividades. En el ámbito de VIII Encuentro, desarrollado en la sede Ciudad Universitaria de Córdoba, se desplegaron numerosas posiciones sobre la enseñanza, la investigación y la formación en historia, teoría y crítica del diseño, la arquitectura y la ciudad; sumándose el aporte realizado a través de sus respectivas conferencias de Ana Clarisa Agüero, Bibiana Cicutti, Fernando Aliata y Alberto Petrina. El conjunto de ponencias que se publican en este Repositorio de la UNC son el resultado de dos intensas jornadas de exposiciones, cuyos contenidos han posibilitado actualizar viejos dilemas y promover nuevos debates. El evento recibió el apoyo de las autoridades de la FAUD-UNC, en especial de la Secretaría de Investigación y de la Biblioteca de nuestra casa, como así también de la Facultad de Arquitectura de la UCC; va para todos ellos un especial agradecimiento

Paisaje de la Antigua Veracruz: Análisis y propuestas para la Casa de Cortés, el Pueblo-Huerto y sus entornos

119 páginas. Especialización en Diseño.En este documento se analiza el paisaje del poblado La Antigua Veracruz y se formula una propuesta para la Casa de Cortés, el pueblo-huerto y sus alrededores. En él se explica el proceso utilizado en el análisis de sus componentes sus potencialidades y sus limitaciones como base para la elaboración de la propuesta paisajista abordada desde diferentes escalas: unidades de paisaje, el pueblo-huerto, que constituye una de las unidades y el sitio como parte integral de un conjunto de monumentos que integran el centro histórico y el jardín de la Casa de Cortés

A Methodology to Determine the Subset of Heuristics for Hyperheuristics through Metalearning for Solving Graph Coloring and Capacitated Vehicle Routing Problems

In this work, we focus on the problem of selecting low-level heuristics in a hyperheuristic approach with offline learning, for the solution of instances of different problem domains. The objective is to improve the performance of the offline hyperheuristic approach, identifying equivalence classes in a set of instances of different problems and selecting the best performing heuristics in each of them. A methodology is proposed as the first step of a set of instances of all problems, and the generic characteristics of each instance and the performance of the heuristics in each one of them are considered to define the vectors of characteristics and make a grouping of classes. Metalearning with statistical tests is used to select the heuristics for each class. Finally, we used the Naive Bayes to test the set instances with k-fold cross-validation, and we compared all results statistically with the best-known values. In this research, the methodology was tested by applying it to the problems of capacitated vehicle routing (CVRP) and graph coloring (GCP). The experimental results show that the proposed methodology can improve the performance of the offline hyperheuristic approach, correctly identifying the classes of instances and applying the appropriate heuristics in each case. This is based on the statistical comparison of the results obtained with those of the state of the art of each instance