Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

JCell : a Java framework for inferring genetic networks

JCell is a framework for reconstructing and simulating genetic networks in the field of molecular biology. It is completely implemented in Java. The main goal of JCell is to gain deep insights of molecular processes within a cell or tissue under various conditions such as drug concentrations or pathogenic mutations. This question has recently become a major area of research in the field of bioinformatics, because understanding the regulating dependencies enables new therapies of diseases like cancer or Alzheimer. To address the mentioned inference problem, several mathematical models and algorithms have been developed and implemented, which try to infer genetic relationships from genomic experiment data. The program consists of a modular structure, which enables users to utilize the framework also in other research areas such as metabolic pathway reconstruction, signalling cascade analysis or general biochemical processes. Further on, JCell can also be used in other contexts to identify dynamic systems from time series data such as financial applications or engineering problems. Usability was always the primary focus during development, so that even users without a strong computer science background are able to use the program. Another focus was the ability of JCell to natively import as much file formats as possible to be compatible with the most commonly used analysis tools. Due to the usage of the programming language Java, the framework is platform independent and thus able to work on most hardware/software systems. This is especially important for those research facilities where no expensive hardware can purchased and where no restrictions for the used operating systems can be implied. Further more, the framework is open to public development and new modules can be easily implemented.JCell ist ein komplett in Java realisiertes Framework zur Rekonstruktion und Simulation von genetischen Netzwerken in verschiedenen Bereichen der Molekularbiologie. Ziel ist die eingehende Untersuchung von Abläufen innerhalb einer Zelle oder eines Gewebetyps bei gleichzeitiger Zugabe von Wirkstoffen oder im Falle von krankhafter Entartung. Diese Fragestellung ist zur Zeit eines der wichtigsten Themengebiete der Bioinformatik, da das Verständnis von genetischer Regulation tiefgreifende Möglichkeiten der Diagnostik und Therapie von Krankheiten wie Krebs oder Alzheimer eröffnet. Zur Lösung des so genannten Netzwerk-Inferenzproblems wurden verschiedene Algorithmen und mathematische Modelle implementiert, die aus gegebenen genomischen Experimentdaten versuchen, regulatorische Interaktionen zu rekonstruieren. Da die gewählte Programmstruktur modular aufgebaut ist, wurden im Laufe der Entwicklung weitere Einsatzgebiete erschlossen. So kann JCell nun auch in anderen Gebieten der Systembiologie, wie zum Beispiel der Forschung im Bereich metabolischer Systeme und der Rekonstruktion von biochemischen Signalwegen innerhalb einer Zelle, eingesetzt werden. Des Weiteren liegen Anfragen von Biotech-Firmen vor, die dynamische Prozesse in biotechnologischen Anlagen besser verstehen wollen. Bei der Entwicklung war stets die einfache Benutzbarkeit der Applikation das primäre Ziel, damit auch Computer-Laien in der Lage sind, das Programm zu bedienen. Ein weiteres Augenmerk lag auf der Implementierung von Methoden zum Einlesen verschiedenster Dateiformate, sodass die gängigsten Analysetools für Genomexperimente unterstützt werden. Durch Verwendung der Programmiersprache Java ist eine weitreichende Plattformunabhängigkeit gewährleistet, sodass JCell auf den meisten Rechnerarchitekturen läuft. Dies hat den Vorteil, dass Anwender keine spezielle Hardware bereitstellen müssen und auch keinerlei Einschränkungen bei der Auswahl eines Betriebssystems haben. Daneben bietet Java noch den Vorteil, dass fremde Entwickler schnell eigene Module in das bestehende Framework einbinden können, was besonders im Hinblick auf die Open-Source-Verfügbarkeit eine wichtige Rolle spielt

High-Quality Hypergraph Partitioning

This dissertation focuses on computing high-quality solutions for the NP-hard balanced hypergraph partitioning problem: Given a hypergraph and an integer $k$, partition its vertex set into $k$ disjoint blocks of bounded size, while minimizing an objective function over the hyperedges. Here, we consider the two most commonly used objectives: the cut-net metric and the connectivity metric. Since the problem is computationally intractable, heuristics are used in practice - the most prominent being the three-phase multi-level paradigm: During coarsening, the hypergraph is successively contracted to obtain a hierarchy of smaller instances. After applying an initial partitioning algorithm to the smallest hypergraph, contraction is undone and, at each level, refinement algorithms try to improve the current solution. With this work, we give a brief overview of the field and present several algorithmic improvements to the multi-level paradigm. Instead of using a logarithmic number of levels like traditional algorithms, we present two coarsening algorithms that create a hierarchy of (nearly) $n$ levels, where $n$ is the number of vertices. This makes consecutive levels as similar as possible and provides many opportunities for refinement algorithms to improve the partition. This approach is made feasible in practice by tailoring all algorithms and data structures to the $n$-level paradigm, and developing lazy-evaluation techniques, caching mechanisms and early stopping criteria to speed up the partitioning process. Furthermore, we propose a sparsification algorithm based on locality-sensitive hashing that improves the running time for hypergraphs with large hyperedges, and show that incorporating global information about the community structure into the coarsening process improves quality. Moreover, we present a portfolio-based initial partitioning approach, and propose three refinement algorithms. Two are based on the Fiduccia-Mattheyses (FM) heuristic, but perform a highly localized search at each level. While one is designed for two-way partitioning, the other is the first FM-style algorithm that can be efficiently employed in the multi-level setting to directly improve $k$-way partitions. The third algorithm uses max-flow computations on pairs of blocks to refine $k$-way partitions. Finally, we present the first memetic multi-level hypergraph partitioning algorithm for an extensive exploration of the global solution space. All contributions are made available through our open-source framework KaHyPar. In a comprehensive experimental study, we compare KaHyPar with hMETIS, PaToH, Mondriaan, Zoltan-AlgD, and HYPE on a wide range of hypergraphs from several application areas. Our results indicate that KaHyPar, already without the memetic component, computes better solutions than all competing algorithms for both the cut-net and the connectivity metric, while being faster than Zoltan-AlgD and equally fast as hMETIS. Moreover, KaHyPar compares favorably with the current best graph partitioning system KaFFPa - both in terms of solution quality and running time

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

Multi-criteria decision-making in whole process design

PhD ThesisIn recent years, the chemical and pharmaceutical industries have faced increased development times and costs with fewer novel chemicals being discovered. This has resulted in many companies focusing on innovative research and development as they consider this key to business success. In particular, a number of leading industrial organisations have adopted the principles of Whole Process Design (WPD). WPD considers the optimisation of the entire product development process, from raw materials to end product, rather than focusing on each individual unit operation. The complexity involved in the implementation of WPD requires rationalised decision-making, often with limited or uncertain information. This thesis assesses the most widely applied methods in Multi-Criteria Decision Analysis (MCDA) in conjunction with the results of two interviews and two questionnaires that identified the industrial requirements for decision-making during WPD. From the findings of this work, a novel decision-making methodology was proposed, the outcome of which allows a decision-maker to visually interpret their decision results with associated levels of uncertainty. To validate the proposed methodology, a software framework was developed that incorporates two other decision-making approaches, the Analytical Hierarchy Process (AHP) and ELimination Et Choix Traduisant la REalité trois (ELECTRE III). The framework was then applied to a number of industrial case studies to validate the application of the proposed methodology.Engineering and Physical Sciences Research Council (EPSRC) and Chemistry Innovatio

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'

In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems