Fast Hierarchical Clustering and Other Applications of Dynamic Closest Pairs

We develop data structures for dynamic closest pair problems with arbitrary distance functions, that do not necessarily come from any geometric structure on the objects. Based on a technique previously used by the author for Euclidean closest pairs, we show how to insert and delete objects from an n-object set, maintaining the closest pair, in O(n log^2 n) time per update and O(n) space. With quadratic space, we can instead use a quadtree-like structure to achieve an optimal time bound, O(n) per update. We apply these data structures to hierarchical clustering, greedy matching, and TSP heuristics, and discuss other potential applications in machine learning, Groebner bases, and local improvement algorithms for partition and placement problems. Experiments show our new methods to be faster in practice than previously used heuristics.Comment: 20 pages, 9 figures. A preliminary version of this paper appeared at the 9th ACM-SIAM Symp. on Discrete Algorithms, San Francisco, 1998, pp. 619-628. For source code and experimental results, see http://www.ics.uci.edu/~eppstein/projects/pairs

CPGA: a two-dimensional, order-based genetic algorithm for cell placement

Not provided

Emergency rapid mapping with drones: models and solution approaches for offline and online mission planning

Die Verfügbarkeit von unbemannten Luftfahrzeugen (unmanned aerial vehicles oder UAVs) und die Fortschritte in der Entwicklung leichtgewichtiger Sensorik eröffnen neue Möglichkeiten für den Einsatz von Fernerkundungstechnologien zur Schnellerkundung in Großschadenslagen. Hier ermöglichen sie es beispielsweise nach Großbränden, Einsatzkräften in kurzer Zeit ein erstes Lagebild zur Verfügung zu stellen. Die begrenzte Flugdauer der UAVs wie auch der Bedarf der Einsatzkräfte nach einer schnellen Ersteinschätzung bedeuten jedoch, dass die betroffenen Gebiete nur stichprobenartig überprüft werden können. In Kombination mit Interpolationsverfahren ermöglichen diese Stichproben anschließend eine Abschätzung der Verteilung von Gefahrstoffen. Die vorliegende Arbeit befasst sich mit dem Problem der Planung von UAV-Missionen, die den Informationsgewinn im Notfalleinsatz maximieren. Das Problem wird dabei sowohl in der Offline-Variante, die Missionen vor Abflug bestimmt, als auch in der Online-Variante, bei der die Pläne während des Fluges der UAVs aktualisiert werden, untersucht. Das übergreifende Ziel ist die Konzeption effizienter Modelle und Verfahren, die Informationen über die räumliche Korrelation im beobachteten Gebiet nutzen, um in zeitkritischen Situationen Lösungen von hoher Vorhersagegüte zu bestimmen. In der Offline-Planung wird das generalized correlated team orienteering problem eingeführt und eine zweistufige Heuristik zur schnellen Bestimmung explorativer UAV-Missionen vorgeschlagen. In einer umfangreichen Studie wird die Leistungsfähigkeit und Konkurrenzfähigkeit der Heuristik hinsichtlich Rechenzeit und Lösungsqualität bestätigt. Anhand von in dieser Arbeit neu eingeführten Benchmarkinstanzen wird der höhere Informationsgewinn der vorgeschlagenen Modelle im Vergleich zu verwandten Konzepten aufgezeigt. Im Bereich der Online-Planung wird die Kombination von lernenden Verfahren zur Modellierung der Schadstoffe mit Planungsverfahren, die dieses Wissen nutzen, um Missionen zu verbessern, untersucht. Hierzu wird eine breite Spanne von Lösungsverfahren aus unterschiedlichen Disziplinen klassifiziert und um neue effiziente Modellierungsvarianten für die Schnellerkundung ergänzt. Die Untersuchung im Rahmen einer ereignisdiskreten Simulation zeigt, dass vergleichsweise einfache Approximationen räumlicher Zusammenhänge in sehr kurzer Zeit Lösungen hoher Qualität ermöglichen. Darüber hinaus wird die höhere Robustheit genauerer, aber aufwändigerer Modelle und Lösungskonzepte demonstriert

Aplicação de meta learning para escolha da melhor meta-heurística em problemas de caixeiro viajante

TCC (graduação) - Universidade Federal de Santa Catarina. Campus Joinville. Engenharia de Transportes e Logística.O presente trabalho tem como objetivo empregar e avaliar estratégias de aprendizado de máquina para escolher meta-heurísticas promissoras para o problema do Traveling Salesman Problem (TSP), o qual caracteriza-se por ser um problema de otimização combinatorial. Na grande maioria das instâncias de TSP não se sabe qual é a solução ótima. Heurísticas e meta-heurísticas são comumente usadas nos problemas de TSP para encontrar soluções de qualidade em um curto período de tempo. Uma vez que diferentes meta-heurísticas podem produzir soluções de qualidade variada, ocorre que não há uma melhor meta-heurística para todas as instâncias. Desse modo, este trabalho explora o uso de métodos de aprendizagem de máquina para criar uma meta-heurística de aprendizagem (meta learning), a fim de identificar quais meta-heurísticas são mais promissoras para solucionar instâncias especificas do TSP, definidas por conjuntos de características (meta features). Com a realização dos experimentos, observou-se que os modelos de meta learning podem prever com precisão quais meta-heurísticas são mais adequadas para diferentes cenários do TSP. Os resultados obtidos dos experimentos também mostram que os métodos de aprendizado utilizados no modelo tem um impacto importante na qualidade das soluções obtidas

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Evaluating the Ability of Conditional Cash Transfers to Promote Human Capital Through the Protection of Health

Conditional cash transfer programs have diffused rapidly across the world since the mid- 1990s and have been adopted as a poverty-reduction tool in the majority of Latin American countries. This dissertation focuses specially on the Colombian conditional cash transfer program, Familias en Acción, and examines the relationship between social welfare policy and health in the Colombian context. The work is divided into three separate papers. Paper 1 assesses the impact of Familias en Acción on adult health outcomes that capture disruptions in daily life due to health conditions. The analysis utilizes data collected by the Colombian National Department of Planning for program evaluation purposes and employs a differences-in-differences approach specifying mixed effects logistic models to examine the impact of the program on impairment, bedridden status, and hospitalization among individuals 18 years of age and older. Paper 2 explores the impact of Familias en Acción on mortality assessing the possibility of differential impacts by age group and cause of death. The work pools program evaluation data with vital statistics and census data to create a dataset of municipal population, municipal death, and municipal exposure to the anti-poverty program. The analysis uses a differences-in- differences approach specifying mixed effects negative binomial regression models. Paper 3 is a qualitative political economy project and draws from both primary as well as secondary data. Familias en Acción was modeled after the Mexican conditional cash transfer program, and the two programs are nearly identical in terms of programmatic components with one major exception which is in the area of health programming. The Mexican program is more comprehensive in the area of health, targeting all ages and offering a more comprehensive package of health services. The work examines the political and economic reasons that led to the adaptation of the Mexican conditional cash transfer program in the Colombian context

On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem

We contribute to the efficient approximation of the Pareto-set for the classical $\mathcal{NP}$-hard multi-objective minimum spanning tree problem (moMST) adopting evolutionary computation. More precisely, by building upon preliminary work, we analyse the neighborhood structure of Pareto-optimal spanning trees and design several highly biased sub-graph-based mutation operators founded on the gained insights. In a nutshell, these operators replace (un)connected sub-trees of candidate solutions with locally optimal sub-trees. The latter (biased) step is realized by applying Kruskal's single-objective MST algorithm to a weighted sum scalarization of a sub-graph. We prove runtime complexity results for the introduced operators and investigate the desirable Pareto-beneficial property. This property states that mutants cannot be dominated by their parent. Moreover, we perform an extensive experimental benchmark study to showcase the operator's practical suitability. Our results confirm that the sub-graph based operators beat baseline algorithms from the literature even with severely restricted computational budget in terms of function evaluations on four different classes of complete graphs with different shapes of the Pareto-front

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum