Injecting problem-dependent knowledge to improve evolutionary optimization search ability

The flexibility introduced by evolutionary algorithms (EAs) has allowed the use of virtually arbitrary objective functions and constraints even when evaluations require, as for real-world problems, running complex mathematical and/or procedural simulations of the systems under analysis. Even so, EAs are not a panacea. Traditionally, the solution search process has been totally oblivious of the specific problem being solved, and optimization processes have been applied regardless of the size, complexity, and domain of the problem. In this paper, we justify our claim that far-reaching benefits may be obtained from more directly influencing how searches are performed. We propose using data mining techniques as a step for dynamically generating knowledge that can be used to improve the efficiency of solution search processes. In this paper, we use Kohonen SOMs and show an application for a well-known benchmark problem in the water distribution system design literature. The result crystallizes the conceptual rules for the EA to apply at certain stages of the evolution, which reduces the search space and accelerates convergence. (C) 2015 Elsevier B.V. All rights reserved.Izquierdo Sebastián, J.; Campbell-Gonzalez, E.; Montalvo Arango, I.; Pérez García, R. (2016). Injecting problem-dependent knowledge to improve evolutionary optimization search ability. Journal of Computational and Applied Mathematics. 291:281-292. doi:10.1016/j.cam.2015.03.019S28129229

A Survey on Scheduling the Task in Fog Computing Environment

With the rapid increase in the Internet of Things (IoT), the amount of data produced and processed is also increased. Cloud Computing facilitates the storage, processing, and analysis of data as needed. However, cloud computing devices are located far away from the IoT devices. Fog computing has emerged as a small cloud computing paradigm that is near to the edge devices and handles the task very efficiently. Fog nodes have a small storage capability than the cloud node but it is designed and deployed near to the edge device so that request must be accessed efficiently and executes in time. In this survey paper we have investigated and analysed the main challenges and issues raised in scheduling the task in fog computing environment. To the best of our knowledge there is no comprehensive survey paper on challenges in task scheduling of fog computing paradigm. In this survey paper research is conducted from 2018 to 2021 and most of the paper selection is done from 2020-2021. Moreover, this survey paper organizes the task scheduling approaches and technically plans the identified challenges and issues. Based on the identified issues, we have highlighted the future work directions in the field of task scheduling in fog computing environment

Community Detection in Networks using Bio-inspired Optimization: Latest Developments, New Results and Perspectives with a Selection of Recent Meta-Heuristics

Detecting groups within a set of interconnected nodes is a widely addressed prob- lem that can model a diversity of applications. Unfortunately, detecting the opti- mal partition of a network is a computationally demanding task, usually conducted by means of optimization methods. Among them, randomized search heuristics have been proven to be efficient approaches. This manuscript is devoted to pro- viding an overview of community detection problems from the perspective of bio-inspired computation. To this end, we first review the recent history of this research area, placing emphasis on milestone studies contributed in the last five years. Next, we present an extensive experimental study to assess the performance of a selection of modern heuristics over weighted directed network instances. Specifically, we combine seven global search heuristics based on two different similarity metrics and eight heterogeneous search operators designed ad-hoc. We compare our methods with six different community detection techniques over a benchmark of 17 Lancichinetti-Fortunato-Radicchi network instances. Ranking statistics of the tested algorithms reveal that the proposed methods perform com- petitively, but the high variability of the rankings leads to the main conclusion: no clear winner can be declared. This finding aligns with community detection tools available in the literature that hinge on a sequential application of different algorithms in search for the best performing counterpart. We end our research by sharing our envisioned status of this area, for which we identify challenges and opportunities which should stimulate research efforts in years to come

Learning From Geometry In Learning For Tactical And Strategic Decision Domains

Artificial neural networks (ANNs) are an abstraction of the low-level architecture of biological brains that are often applied in general problem solving and function approximation. Neuroevolution (NE), i.e. the evolution of ANNs, has proven effective at solving problems in a variety of domains. Information from the domain is input to the ANN, which outputs its desired actions. This dissertation presents a new NE algorithm called Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT), based on a novel indirect encoding of ANNs. The key insight in HyperNEAT is to make the algorithm aware of the geometry in which the ANNs are embedded and thereby exploit such domain geometry to evolve ANNs more effectively. The dissertation focuses on applying HyperNEAT to tactical and strategic decision domains. These domains involve simultaneously considering short-term tactics while also balancing long-term strategies. Board games such as checkers and Go are canonical examples of such domains; however, they also include real-time strategy games and military scenarios. The dissertation details three proposed extensions to HyperNEAT designed to work in tactical and strategic decision domains. The first is an action selector ANN architecture that allows the ANN to indicate its judgements on every possible action all at once. The second technique is called substrate extrapolation. It allows learning basic concepts at a low resolution, and then increasing the resolution to learn more advanced concepts. The iii final extension is geometric game-tree pruning, whereby HyperNEAT can endow the ANN the ability to focus on specific areas of a domain (such as a checkers board) that deserve more inspection. The culminating contribution is to demonstrate the ability of HyperNEAT with these extensions to play Go, a most challenging game for artificial intelligence, by combining HyperNEAT with UC

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Challenges and prospects of spatial machine learning

The main objective of this thesis is to improve the usefulness of spatial machine learning for the spatial sciences and to allow its unused potential to be exploited. To achieve this objective, this thesis addresses several important but distinct challenges which spatial machine learning is facing. These are the modeling of spatial autocorrelation and spatial heterogeneity, the selection of an appropriate model for a given spatial problem, and the understanding of complex spatial machine learning models.Das wesentliche Ziel dieser Arbeit ist es, die Nützlichkeit des räumlichen maschinellen Lernens für die Raumwissenschaften zu verbessern und es zu ermöglichen, ungenutztes Potenzial auszuschöpfen. Um dieses Ziel zu erreichen, befasst sich diese Arbeit mit mehreren wichtigen Herausforderungen, denen das räumliche maschinelle Lernen gegenübersteht. Diese sind die Modellierung von räumlicher Autokorrelation und räumlicher Heterogenität, die Auswahl eines geeigneten Modells für ein gegebenes räumliches Problem und das Verständnis komplexer räumlicher maschineller Lernmodelle

Towards Visualization of Discrete Optimization Problems and Search Algorithms

Diskrete Optimierung beschäftigt sich mit dem Identifizieren einer Kombination oder Permutation von Elementen, die im Hinblick auf ein gegebenes quantitatives Kriterium optimal ist. Anwendungen dafür entstehen aus Problemen in der Wirtschaft, der industriellen Fertigung, den Ingenieursdisziplinen, der Mathematik und Informatik. Dazu gehören unter anderem maschinelles Lernen, die Planung der Reihenfolge und Terminierung von Fertigungsprozessen oder das Layout von integrierten Schaltkreisen. Häufig sind diskrete Optimierungsprobleme NP-hart. Dadurch kommt der Erforschung effizienter, heuristischer Suchalgorithmen eine große Bedeutung zu, um für mittlere und große Probleminstanzen überhaupt gute Lösungen finden zu können. Dabei wird die Entwicklung von Algorithmen dadurch erschwert, dass Eigenschaften der Probleminstanzen aufgrund von deren Größe und Komplexität häufig schwer zu identifizieren sind. Ebenso herausfordernd ist die Analyse und Evaluierung von gegebenen Algorithmen, da das Suchverhalten häufig schwer zu charakterisieren ist. Das trifft besonders im Fall von emergentem Verhalten zu, wie es in der Forschung der Schwarmintelligenz vorkommt. Visualisierung zielt auf das Nutzen des menschlichen Sehens zur Datenverarbeitung ab. Das Gehirn hat enorme Fähigkeiten optische Reize von den Sehnerven zu analysieren, Formen und Muster darin zu erkennen, ihnen Bedeutung zu verleihen und dadurch ein intuitives Verstehen des Gesehenen zu ermöglichen. Diese Fähigkeit kann im Speziellen genutzt werden, um Hypothesen über komplexe Daten zu generieren, indem man sie in einem Bild repräsentiert und so dem visuellen System des Betrachters zugänglich macht. Bisher wurde Visualisierung kaum genutzt um speziell die Forschung in diskreter Optimierung zu unterstützen. Mit dieser Dissertation soll ein Ausgangspunkt geschaffen werden, um den vermehrten Einsatz von Visualisierung bei der Entwicklung von Suchheuristiken zu ermöglichen. Dazu werden zunächst die zentralen Fragen in der Algorithmenentwicklung diskutiert und daraus folgende Anforderungen an Visualisierungssysteme abgeleitet. Mögliche Forschungsrichtungen in der Visualisierung, die konkreten Nutzen für die Forschung in der Optimierung ergeben, werden vorgestellt. Darauf aufbauend werden drei Visualisierungssysteme und eine Analysemethode für die Erforschung diskreter Suche vorgestellt. Drei wichtige Aufgaben von Algorithmendesignern werden dabei adressiert. Zunächst wird ein System für den detaillierten Vergleich von Algorithmen vorgestellt. Auf der Basis von Zwischenergebnissen der Algorithmen auf einer Probleminstanz wird der Suchverlauf der Algorithmen dargestellt. Der Fokus liegt dabei dem Verlauf der Qualität der Lösungen über die Zeit, wobei die Darstellung durch den Experten mit zusätzlichem Wissen oder Klassifizierungen angereichert werden kann. Als zweites wird ein System für die Analyse von Suchlandschaften vorgestellt. Auf Basis von Pfaden und Abständen in der Landschaft wird eine Karte der Probleminstanz gezeichnet, die strukturelle Merkmale intuitiv erfassbar macht. Der zweite Teil der Dissertation beschäftigt sich mit der topologischen Analyse von Suchlandschaften, aufbauend auf einer Schwellwertanalyse. Ein Visualisierungssystem wird vorgestellt, dass ein topologisch equivalentes Höhenprofil der Suchlandschaft darstellt, um die topologische Struktur begreifbar zu machen. Dieses System ermöglicht zudem, den Suchverlauf eines Algorithmus direkt in der Suchlandschaft zu beobachten, was insbesondere bei der Untersuchung von Schwarmintelligenzalgorithmen interessant ist. Die Berechnung der topologischen Struktur setzt eine vollständige Aufzählung aller Lösungen voraus, was aufgrund der Größe der Suchlandschaften im allgemeinen nicht möglich ist. Um eine Anwendbarkeit der Analyse auf größere Probleminstanzen zu ermöglichen, wird eine Methode zur Abschätzung der Topologie vorgestellt. Die Methode erlaubt eine schrittweise Verfeinerung der topologischen Struktur und lässt sich heuristisch steuern. Dadurch können Wissen und Hypothesen des Experten einfließen um eine möglichst hohe Qualität der Annäherung zu erreichen bei gleichzeitig überschaubarem Berechnungsaufwand.Discrete optimization deals with the identification of combinations or permutations of elements that are optimal with regard to a specific, quantitative criterion. Applications arise from problems in economy, manufacturing, engineering, mathematics and computer sciences. Among them are machine learning, scheduling of production processes, and the layout of integrated electrical circuits. Typically, discrete optimization problems are NP hard. Thus, the investigation of efficient, heuristic search algorithms is of high relevance in order to find good solutions for medium- and large-sized problem instances, at all. The development of such algorithms is complicated, because the properties of problem instances are often hard to identify due to the size and complexity of the instances. Likewise, the analysis and evaluation of given algorithms is challenging, because the search behavior of an algorithm is hard to characterize, especially in case of emergent behavior as investigated in swarm intelligence research. Visualization targets taking advantage of human vision in order to do data processing. The visual brain possesses tremendous capabilities to analyse optical stimulation through the visual nerves, perceive shapes and patterns, assign meaning to them and thus facilitate an intuitive understanding of the seen. In particular, this can be used to generate hypotheses about complex data by representing them in a well-designed depiction and making it accessible to the visual system of the viewer. So far, there is only little use of visualization to support the discrete optimization research. This thesis is meant as a starting point to allow for an increased application of visualization throughout the process of developing discrete search heuristics. For this, we discuss the central questions that arise from the development of heuristics as well as the resulting requirements on visualization systems. Possible directions of research for visualization are described that yield a specific benefit for optimization research. Based on this, three visualization systems and one analysis method are presented. These address three important tasks of algorithm designers. First, a system for the fine-grained comparison of algorithms is introduced. Based on the intermediate results of algorithm runs on a given problem instance the search process is visualized. The focus is on the progress of the solution quality over time while allowing the algorithm expert to augment the depiction with additional domain knowledge and classification of individual solutions. Second, a system for the analysis of search landscapes is presented. Based on paths and distances in the landscape, a map of the problem instance is drawn that facilitates an intuitive cognition of structural properties. The second part of this thesis focuses on the topological analysis of search landscapes, based on barriers. A visualization system is presented that shows a topological equivalent height profile of the search landscape. Further, the system facilitates to observe the search process of an algorithm directly within the search landscape. This is of particular interest when researching swarm intelligence algorithms. The computation of topological structure requires a complete enumeration of all solutions which is not possible in the general case due to the size of the search landscapes. In order to enable an application to larger problem instances, we introduce a method to approximate the topological structure. The method allows for an incremental refinement of the topological approximation that can be controlled using a heuristic. Thus, the domain expert can introduce her knowledge and also hypotheses about the problem instance into the analysis so that an approximation of good quality is achieved with reasonable computational effort

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions