A grouping hyper-heuristic framework: application on graph colouring

Grouping problems are hard to solve combinatorial optimisation problems which require partitioning of objects into a minimum number of subsets while a given objective is simultaneously optimised. Selection hyper-heuristics are high level general purpose search methodologies that operate on a space formed by a set of low level heuristics rather than solutions. Most of the recently proposed selection hyper-heuristics are iterative and make use of two key methods which are employed successively; heuristic selection and move acceptance. In this study, we present a novel generic selection hyper-heuristic framework containing a fixed set of reusable grouping low level heuristics and an unconventional move acceptance mechanism for solving grouping problems. This framework deals with one solution at a time at any given decision point during the search process. Also, a set of high quality solutions, capturing the trade-off between the number of groups and the additional objective for the given grouping problem, is maintained. The move acceptance mechanism embeds a local search approach which is capable of progressing improvements on those trade-off solutions. The performance of different selection hyper-heuristics with various components under the proposed framework is investigated on graph colouring as a representative grouping problem. Then, the top performing hyper-heuristics are applied to a benchmark of examination timetabling instances. The empirical results indicate the effectiveness and generality of the proposed framework enabling grouping hyper-heuristics to achieve high quality solutions in both domains. ©2015 Elsevier Ltd. All rights reserved

CHAMP: Creating Heuristics via Many Parameters for online bin packing

The online bin packing problem is a well-known bin packing variant which requires immediate decisions to be made for the placement of a lengthy sequence of arriving items of various sizes one at a time into fixed capacity bins without any overflow. The overall goal is maximising the average bin fullness. We investigate a ‘policy matrix’ representation which assigns a score for each decision option independently and the option with the highest value is chosen for one dimensional online bin packing. A policy matrix might also be considered as a heuristic with many parameters, where each parameter value is a score. We hence investigate a framework which can be used for creating heuristics via many parameters. The proposed framework combines a Genetic Algorithm optimiser, which searches the space of heuristics in policy matrix form, and an online bin packing simulator, which acts as the evaluation function. The empirical results indicate the success of the proposed approach, providing the best solutions for almost all item sequence generators used during the experiments. We also present a novel fitness landscape analysis on the search space of policies. This study hence gives evidence of the potential for automated discovery by intelligent systems of powerful heuristics for online problems; reducing the need for expensive use of human expertise

Heuristic generation via parameter tuning for online bin packing

Online bin packing requires immediate decisions to be made for placing an incoming item one at a time into bins of fixed capacity without causing any overflow. The goal is to maximise the average bin fullness after placement of a long stream of items. A recent work describes an approach for solving this problem based on a ‘policy matrix’ representation in which each decision option is independently given a value and the highest value option is selected. A policy matrix can also be viewed as a heuristic with many parameters and then the search for a good policy matrix can be treated as a parameter tuning process. In this study, we show that the Irace parameter tuning algorithm produces heuristics which outperform the standard human designed heuristics for various instances of the online bin packing problem

Large-scale optimization : combining co-operative coevolution and fitness inheritance

Large-scale optimization, here referring mainly to problems with many design parameters remains a serious challenge for optimization algorithms. When the problem at hand does not succumb to analytical treatment (an overwhelmingly common place situation), the engineering and adaptation of stochastic black box optimization methods tends to be a favoured approach, particularly the use of Evolutionary Algorithms (EAs). In this context, many approaches are currently under investigation for accelerating performance on large-scale problems, and we focus on two of those in this research. The first is co-operative co-evolution (CC), where the strategy is to successively optimize only subsets of the design parameters at a time, keeping the remainder fixed, with an organized approach to managing and reconciling these subspace optimization. The second is fitness inheritance (FI), which is essentially a very simple surrogate model strategy, in which, with some probability, the fitness of a solution is simply guessed to be a simple function of the finesses of that solution’s parents. Both CC and FI have been found successful on nontrivial and multiple test cases, and they use fundamentally distinct strategies. In this thesis, we explored the extent to which both of these strategies can be used to provide additional benefits. In addition to combining CC and FI, this thesis also introduces a new FI scheme which further improves the performance of CC-FI. We show that the new algorithm CC-FI is highly effective for solving problems, especially when the new FI scheme is used. In the thesis, we also explored two basic adaptive parameter setting strategies for the FI component. We found that engineering FI (and CC, where it was otherwise not present) into these algorithms led to good performance and results

Evolutionary algorithms and hyper-heuristics for orthogonal packing problems

This thesis investigates two major classes of Evolutionary Algorithms, Genetic Algorithms (GAs) and Evolution Strategies (ESs), and their application to the Orthogonal Packing Problems (OPP). OPP are canonical models for NP-hard problems, the class of problems widely conceived to be unsolvable on a polynomial deterministic Turing machine, although they underlie many optimisation problems in the real world. With the increasing power of modern computers, GAs and ESs have been developed in the past decades to provide high quality solutions for a wide range of optimisation and learning problems. These algorithms are inspired by Darwinian nature selection mechanism that iteratively select better solutions in populations derived from recombining and mutating existing solutions. The algorithms have gained huge success in many areas, however, being stochastic processes, the algorithms' behaviour on different problems is still far from being fully understood. The work of this thesis provides insights to better understand both the algorithms and the problems. The thesis begins with an investigation of hyper-heuristics as a more general search paradigm based on standard EAs. Hyper-heuristics are shown to be able to overcome the difficulty of many standard approaches which only search in partial solution space. The thesis also looks into the fundamental theory of GAs, the schemata theorem and the building block hypothesis, by developing the Grouping Genetic Algorithms (GGA) for high dimensional problems and providing supportive yet qualified empirical evidences for the hypothesis. Realising the difficulties of genetic encoding over combinatorial search domains, the thesis proposes a phenotype representation together with Evolution Strategies that operates on such representation. ESs were previously applied mainly to continuous numerical optimisation, therefore being less understood when searching in combinatorial domains. The work in this thesis develops highly competent ES algorithms for OPP and opens the door for future research in this area

Um algoritmo evolutivo baseado em heurísticas construtivas para problemas de agrupamento aplicado à PCR multiplex

Polymerase chain reaction (PCR) is one of the most widely used molecular biology methods in clinical and research laboratories. Through it, it is possible to generate billions of copies of a given DNA fragment in a short period of time. The potential of this reaction and its variants is explored in a wide range of applications in different scientific fields, which motivates research aimed at optimizing PCR assays. Multiplex PCR is a variation of conventional PCR that allows the amplification of multiple specific DNA fragments in the same assay, saving time, costs, and especially samples of genetic material. Designing this reaction is especially challenging because it requires the difficult task of efficiently grouping the amplifications according to the compatibility of their components. This task is usually abstracted to a combinatorial problem about grouping the primer pairs used, which consist of short sequences of DNA synthesized to delimit specifically each target fragment. In this work, a computational model was developed to solve the multiplex PCR grouping problem aiming at an approach capable of dealing with interests frequently approached in isolation by other works. They are: the use of a robust computational strategy as opposed to deterministic algorithms; the applicability of the model in contexts requiring grouping of amplifications in multiple multiplex PCR tubes; minimizing the number of tubes required; the dissociation of the search method from the set of compatibility measures adopted; and the ability to handle the more complex scenario in which two or more options of primer pairs are provided by amplification, expanding the universe of possibilities and potentially the quality of the results. The construction of the model was inspired by methods previously applied to the known bin packing problem. Thus, an evolutionary algorithm is adapted to the search for specific element permutations aiming at the subsequent decoding of the solutions through a building heuristic. Besides, is presented a search space restriction process that allowed to improve the optimization performance. The approach was initially adapted to the bin packing problem, which allowed an evaluation based on benchmarks widely explored in the literature. In this case, the comparative analysis presented points to the competitiveness of the developed model in relation to the referenced algorithms. Subsequently, the proposal was adapted to the multiplex PCR problem. The results of exploratory experiments conducted indicate the scalability of the model and highlight the relevant contribution arising from the breadth of the approach. Finally, it is presented a comparative experimental analysis with the MultiPLX program, which is available for multiplex PCR design and is based on a similar problem formulation. The results obtained by the developed algorithm were superior in the three considered cases, reinforcing the applicability of the proposed model.A reação em cadeia da polimerase (Polymerase Chain Reaction, PCR) é um dos métodos de biologia molecular mais utilizados em laboratórios clínicos e de pesquisa. Com a PCR é possível gerar bilhões de cópias de um determinado fragmento de DNA em um curto período de tempo. O potencial dessa reação e de suas variantes é explorado em um vasto conjunto de aplicações inseridas em diferentes campos científicos, motivando a pesquisa direcionada à otimização dos ensaios de PCR. A PCR multiplex consiste em uma variação da PCR convencional que permite a amplificação de múltiplos fragmentos específicos de DNA em um mesmo tubo, propiciando economia de tempo, custos e principalmente amostras do material genético. O projeto da reação é especialmente desafiador na medida em que exige a difícil tarefa de agrupar as amplificações de acordo com a compatibilidade dos componentes envolvidos. Geralmente, métodos in silico para essa reação são baseados no problema combinatório decorrente do agrupamento dos pares de primers utilizados, que consistem em sequências curtas de DNA sintetizadas para delimitar especificamente cada fragmento alvo. Neste trabalho, foi desenvolvido um modelo computacional para o problema de agrupamento da PCR multiplex, visando uma abordagem que compreenda, simultaneamente, aspectos determinantes para a aplicabilidade do modelo e a otimização eficiente da reação, a saber: o tratamento de problemas que exijam múltiplos tubos de PCR multiplex para a cobertura dos alvos; a minimização do número de tubos necessários; a utilização de uma estratégia de busca estocástica em oposição a algoritmos determinísticos; o desacoplamento do método de busca em relação ao conjunto de medidas de compatibilidade adotadas; e a capacidade de tratar o cenário mais complexo em que duas ou mais opções pares de primers são fornecidas por amplificação. O modelo é composto pela adaptação de um algoritmo evolutivo à busca de permutações dos elementos e uma heurística construtiva responsável pela decodificação das soluções mapeadas. Além disso, um processo de restrição do espaço de busca é implementado visando aprimorar o desempenho da busca. A construção da proposta foi inspirada em métodos desenvolvidos para a solução do conhecido problema do empacotamento, o que permitiu uma avaliação inicial baseada em benchmarks amplamente explorados na literatura. Nesse caso, a análise comparativa apresentada evidencia a competitividade do modelo desenvolvido diante dos algoritmos referenciados. Posteriormente, o modelo foi adaptado para a otimização da PCR multiplex. Os resultados de experimentos exploratórios realizados indicam a escalabilidade do modelo e ressaltam a relevante contribuição decorrente da amplitude da abordagem. Finalmente, é apresentada uma análise experimental comparativa com o programa MultiPLX, que baseia-se em uma formulação semelhante do problema. Os resultados superiores obtidos pelo algoritmo proposto reforçam a aplicabilidade do modelo desenvolvido.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Evolutionary algorithms and hyper-heuristics for orthogonal packing problems

This thesis investigates two major classes of Evolutionary Algorithms, Genetic Algorithms (GAs) and Evolution Strategies (ESs), and their application to the Orthogonal Packing Problems (OPP). OPP are canonical models for NP-hard problems, the class of problems widely conceived to be unsolvable on a polynomial deterministic Turing machine, although they underlie many optimisation problems in the real world. With the increasing power of modern computers, GAs and ESs have been developed in the past decades to provide high quality solutions for a wide range of optimisation and learning problems. These algorithms are inspired by Darwinian nature selection mechanism that iteratively select better solutions in populations derived from recombining and mutating existing solutions. The algorithms have gained huge success in many areas, however, being stochastic processes, the algorithms' behaviour on different problems is still far from being fully understood. The work of this thesis provides insights to better understand both the algorithms and the problems. The thesis begins with an investigation of hyper-heuristics as a more general search paradigm based on standard EAs. Hyper-heuristics are shown to be able to overcome the difficulty of many standard approaches which only search in partial solution space. The thesis also looks into the fundamental theory of GAs, the schemata theorem and the building block hypothesis, by developing the Grouping Genetic Algorithms (GGA) for high dimensional problems and providing supportive yet qualified empirical evidences for the hypothesis. Realising the difficulties of genetic encoding over combinatorial search domains, the thesis proposes a phenotype representation together with Evolution Strategies that operates on such representation. ESs were previously applied mainly to continuous numerical optimisation, therefore being less understood when searching in combinatorial domains. The work in this thesis develops highly competent ES algorithms for OPP and opens the door for future research in this area

Bioinformatic Analysis of a Set of 14 Temperate Bacteriophages Isolated from Staphylococcus aureus Strains Highlights Their Massive Genetic Diversity

Epidemiology and virulence studies of Staphylococcus aureus showed that temperate bacteriophages are one of the most powerful drivers for its evolution not only because of their abundance but also because of the richness of their genetic payload. Here, we report the isolation, genome sequencing, and bioinformatic analysis of 14 bacteriophages induced from lysogenic S. aureus strains from human or veterinary (cattle) origin. The bacteriophages belonged to the Siphoviridae family; were of similar genome size (40 to 45 kbp); and fell into clusters B2, B3, B5, and B7 according to a recent clustering proposal. One of the phages, namely, vB_SauS_308, was the most unusual one, belonging to the sparsely populated subcluster B7 but showing differences in protein family contents compared with the rest of the members. This phage contains a type I endolysin (one catalytic domain and noncanonical cell wall domain [CBD]) and a host recognition module lacking receptor binding protein, cell wall hydrolase, and tail fiber proteins. This phage also lacked virulence genes, which is opposite to what has been reported for subcluster B6 and B7 members. None of six phages, taken as representatives of each of the four subclusters, showed activity on coagulase-negative staphylococci (excepted for two Staphylococcus hominis strains in which propagation and a very slow adsorption rate were observed) nor transducing ability. Immunity tests on S. aureus RN4220 lysogens with each of these phages showed no cross immunity.Fil: Suárez, Cristian Alejandro. Universidad Nacional de Rosario. Facultad de Ciencias Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaFil: Carrasco, Soledad Telma. Universidad Nacional de Rosario. Facultad de Ciencias Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaFil: Brandolisio, Facundo Nahuel Adrián. Universidad Nacional de Rosario. Facultad de Ciencias Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaFil: Abatángelo, Virginia. Universidad Nacional de Rosario. Facultad de Ciencias Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaFil: Boncompain, Carina Andrea. Universidad Nacional de Rosario. Facultad de Ciencias Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaFil: Peressutti Bacci, Natalia. Universidad Nacional de Rosario. Facultad de Ciencias Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; ArgentinaFil: Carrasco, Soledad Telma. Universidad Nacional de Rosario. Facultad de Ciencias Médicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentin

A Scalability Study of Evolutionary Algorithms for Clustering

Evolutionäre Algorithmen (EA) sind Optimierungswerkzeuge, welche auf Darwins Evolutionstheorie und Mendels Genetik basieren. In ihrer über 30-jährigen Geschichte, haben sie sich einen Ruf als gute Löser für schwere Probleme erarbeitet. Diese Diplomarbeit betrachtet die Skalierbarkeit von EAs und ihre Anwendbarkeit auf große Probleme. Literatur zu diesem Thema wird in vier Gruppen vorgestellt: * Ansätze zur Verbesserung allgemein anwendbarer EAs, welche auf einer Hypothese über die den EAs zu Grunde liegende Theorie basieren (Building Block Hypothese); * parallele EAs, welche die Ausführungszeit unter Einsatz zusätzlicher Hardware verbessern; * EAs die problemspezifische Operatoren verwenden; und * mehrstufige Systeme, welche EA beinhalten. Die Ansätze der ersten beiden Gruppen haben flexible Algorithmen zum Ziel, welche leicht auf eine Vielzahl von Problemen angewendet werden können. Die beiden letztgenannten Ansätze opfern diese Flexibilität zu Gunsten verbesserter Performanz auf einem spezifischen Problembereich. Diese Arbeit untersucht experimentell die Skalierbarkeit von evolutionären Clustering-Algorithmen. Clustering Probleme sind allgemein und auch speziell zur Untersuchung von EAs von Interesse. Der allgemeine Reiz von Clustering liegt in der verbreiteten Anwendung in vielen Wissenschaftsbereichen; das Clustering Problem ist nicht nur auf die Informatik beschränkt. Diese Arbeit betrachtet Clustering basierend auf paarweisen Ähnlichkeiten, welches die allgemeinste Modellierung des Problems ist. Für die Analyse von EAs ist Clustering auf Grund der verwendeten Repräsentation von Lösungskandidaten interessant. Die gewählte Kodierung führt zu einer hohen Abhängigkeit zwischen vielen Variablen innerhalb eines Lösungskandidaten; dies erhöht die Schwierigkeit des Problems für EAs. Zur experimentellen Skalierbarkeitsanalyse sind skalierbare Testdaten notwendig, welche als dünn-besetzte paarweise Ähnlichkeitsmatritzen erstellt werden. Ein einfacher EA, welcher als Referenz eingeführt wird, zeigt schlechte Skalierbarkeitseigenschaften für diese Probleme. Mit wachsender Problemgröße nimmt die Laufzeit schneller als quadratisch zu. Schon für ein Problem mit 2.000 Objekten beträgt die durchschnittliche Laufzeit bis zum Erreichen zufriedenstellender Lösungen über 20 Minuten. Dadurch ist der Referenzalgorithmus für große Probleme nicht geeignet. Verschiedene Erweiterungen des Referenzalgorithmus werden vorgeschlagen. Diese integrieren problemspezifisches Wissen in Form von speziellen Rekombinationsoperatoren und durch die Hybridisierung mit Cluster-Heuristiken. Insgesamt ergeben sich durch Kombinationen der vorgeschlagenen Operatoren 126 verschiedene Algorithmenkonfigurationen, welche für Probleme mit bis zu 2.000 Objekten getestet werden. Als Ergebnis der Experimente lässt sich feststellen, dass eine intelligente Initialisierung alleine, ohne Hybridisierung und mit Standard-Rekombinationsoperatoren, keine verbesserte Skalierbarkeit erreichen kann. Es finden sich aber Algorithmen, welche durch Cluster-basierte Rekombination oder durch die Hybridisierung mit einem hill-climbing Algorithmus. So ist es möglich, Probleme mit 2.000 Objekten durchschnittlich in unter drei Sekunden zu lösen. Es werden Laufzeiten erreicht, die fast linear mit der Problemgröße skalieren. Probleme mit bis zu 100.000 Objekten werden mit einer durchschnittlichen Laufzeit von deutlich unter 1.000 Sekunden gelöst. Die Algorithmuskonfigurationen die mit guter Performanz gemessen wurden, werden im nächsten Schritt erweitert. Die Verbesserungen basieren auf bekannten zweistufigen Clustering EAs. Die vorgeschlagenen Verfahren clustern in der ersten Stufe ein größenreduziertes Problem mit einem EA. Anschließend wird die berechnete Population verwendet, um den EA der zweiten Stufe zu initialisieren, welcher dann auf dem original Problem arbeitet. Zur Größenreduktion werden zwei Möglichkeiten vorgeschlagen: die Komprimierung des Suchraums durch das Zusammenfassen von Objekten zu Objektgruppen und das Zerlegen des Problems in mehrere kleinere Probleme, welche unabhängig voneinander in der ersten Stufe bearbeitet werden. Die experimentelle Auswertung zeigt, dass der Ansatz mit Objektgruppen Potential zur weiteren Reduzierung der Laufzeit hat, während das Zerlegen des Problems die Laufzeit nicht weiter verbessert. Der Test des zweistufigen Ansatzes mit guten Algorithmuskonfigurationen zeigt eine verringerte Robustheit, da nun manche zuvor erfolgreiche Konfiguration regelmäßig nur lokale Optima erreicht. Andere Konfigurationen hingegen zeigen beträchtliche Verbesserungen der Laufzeit, z.B. erreicht die beste Konfiguration beständig zufriedenstellende Lösungen mit nur 30% der Laufzeit, die ein einstufiger EA in der selben Konfiguration benötigt. Probleme mit bis zu 100.000 Objekten können so mit einer durchschnittlichen Laufzeit von 200 Sekunden gelöst werden. Abschließend lässt sich sagen, dass für die Clusteringprobleme evolutionäre Ansätze als Basis für erfolgreiche, gut skalierende Methoden dienen können. Dies setzt jedoch die Integration von problemspezifischem Wissen an passenden Stellen voraus. Obwohl ein Standard EA flexibel genug ist, um ohne großen Aufwand für Clustering Probleme angepasst zu werden, sind Standard Operatoren nicht ausreichend um gute Leistung oder Skalierbarkeit zu erzielen. Der Standard EA ist nicht geeignet zum Lösen großer Probleme. Sollen große Probleme mit wenig Aufwand gelöst werden, so kann der Entwurf von problemspezifischen Operatoren zu kostspielig sein. Hier empfiehlt es sich den Algorithmenentwurf auf einer problemspezifischen Heuristik aufzubauen, auch wenn die Heuristik anfällig ist nur lokale Optima zu erreichen. In Kombination mit einem EA hat dieser Ansatz trotzdem gute Resultate gezeigt: der hybride Algorithmus ist zum einen schneller als ein Standard EA im Erfolgsfall und zum anderen erfolgreicher als die nicht-hybride Anwendung der Heuristik. Ist jedoch bestmögliche Leistung ein Hauptaugenmerk, so kann der hybride Algorithmus weiter optimiert werden, indem problemspezifische Operatoren oder zweistufige Verfahren eingeführt werden. Diese Optimierungen können die Leistung nochmals beträchtlich verbessern. Jedoch sind sie aufwändiger zu entwickeln und erhöhen die Fehleranfälligkeit auf Grund reduzierter Robustheit