Exact Computation of the Fitness-Distance Correlation for Pseudoboolean Functions with One Global Optimum

Chicano, F., & Alba E. (2012). Exact Computation of the Fitness-Distance Correlation for Pseudoboolean Functions with One Global Optimum. (Hao, J-K., & Middendorf M., Ed.).Evolutionary Computation in Combinatorial Optimization - 12th European Conference, EvoCOP 2012, Málaga, Spain, April 11-13, 2012. Proceedings. 111–123.Landscape theory provides a formal framework in which combinatorial optimization problems can be theoretically characterized as a sum of a special kind of landscapes called elementary landscapes. The decomposition of the objective function of a problem into its elementary components can be exploited to compute summary statistics. We present closed-form expressions for the fitness-distance correlation (FDC) based on the elementary landscape decomposition of the problems defined over binary strings in which the objective function has one global optimum. We present some theoretical results that raise some doubts on using FDC as a measure of problem difficulty.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Spanish Ministry of Science and Innovation and FEDER under contracts TIN2008-06491-C04-01 and TIN2011-28194. Andalusian Government under contract P07-TIC-03044

Técnicas metaheurísticas avanzadas aplicadas a la resolución de problemas bioinformáticos

La finalidad de esta línea de investigación es el estudio y resolución de problemas del area Bioinformática mediante la utilización de métodos inteligentes. Particularmente, nuestro trabajo se enfoca en la resolución de problemas de secuenciamiento de un genoma por medio del diseño e implementación de nuevas técnicas metaheurísticas ya sean basadas en trayectoria como en población. También consideramos la posibilidad de hibridar y/o distribuir estos métodos dependiendo de la complejidad del problema a resolver.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

An App Performance Optimization Advisor for Mobile Device App Marketplaces

On mobile phones, users and developers use apps official marketplaces serving as repositories of apps. The Google Play Store and Apple Store are the official marketplaces of Android and Apple products which offer more than a million apps. Although both repositories offer description of apps, information concerning performance is not available. Due to the constrained hardware of mobile devices, users and developers have to meticulously manage the resources available and they should be given access to performance information about apps. Even if this information was available, the selection of apps would still depend on user preferences and it would require a huge cognitive effort to make optimal decisions. Considering this fact we propose APOA, a recommendation system which can be implemented in any marketplace for helping users and developers to compare apps in terms of performance. APOA uses as input metric values of apps and a set of metrics to optimize. It solves an optimization problem and it generates optimal sets of apps for different user's context. We show how APOA works over an Android case study. Out of 140 apps, we define typical usage scenarios and we collect measurements of power, CPU, memory, and network usages to demonstrate the benefit of using APOA.Comment: 18 pages, 8 figure

Uso de técnicas metaheurísticas avanzadas para resolver problemas de optimización combinatoria

La finalidad de esta línea de investigación es el estudio y resolución de problemas de optimización combinatoria mediante la utilización de métodos aproximados. Particularmente, nuestro trabajo se enfoca en el análisis y desarrollo de algoritmos metaheurísticos basados en trayectoria y en población, así como también híbridos, que permitan resolver eficientemente problemas genéricos como es el caso de QAP y problemas específicos y del mundo real como FAP y TSP. También consideramos la posibilidad de distribuir y/o paralelizar estos métodos dependiendo de la complejidad del problema a resolver.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Using a Parallel Ensemble of Sequence-Based Selection Hyper-Heuristics for Electric Bus Scheduling

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordA Sequence-based Selection Hyper-Heuristic (SSHH) utilises a hidden Markov model (HMM) to generate sequences of low-level heuristics to apply to a given problem. The HMM represents learnt probabilistic relationships in transitioning from one heuristic to the next for generating good sequences. However, a single HMM will only represent one learnt behaviour pattern which may not be ideal. Furthermore, using a single HMM to generate sequences is sequential in manner but most processors are parallel in nature. Consequently, this paper proposes that the effectiveness and speed of SSHH can be improved by using multiple SSHH, an ensemble. These will be able to operate in parallel exploiting multi-core processor resources facilitating faster optimisation. Two methods of parallel ensemble SSHH are investigated, sharing the best found solution amongst SSHH instantiations or combining HMM information between SSHH models. The effectiveness of the methods are assessed using a real-world electric bus scheduling optimisation problem. Sharing best found solutions between ensembles of SSHH models that have differing sequence behaviours significantly improved upon sequential SSHH results with much lower run-times.Innovate UKCity Scienc

A Maximum Satisfiability Based Approach to Bi-Objective Boolean Optimization

Many real-world problem settings give rise to NP-hard combinatorial optimization problems. This results in a need for non-trivial algorithmic approaches for finding optimal solutions to such problems. Many such approaches—ranging from probabilistic and meta-heuristic algorithms to declarative programming—have been presented for optimization problems with a single objective. Less work has been done on approaches for optimization problems with multiple objectives. We present BiOptSat, an exact declarative approach for finding so-called Pareto-optimal solutions to bi-objective optimization problems. A bi-objective optimization problem arises for example when learning interpretable classifiers and the size, as well as the classification error of the classifier should be taken into account as objectives. Using propositional logic as a declarative programming language, we seek to extend the progress and success in maximum satisfiability (MaxSAT) solving to two objectives. BiOptSat can be viewed as an instantiation of the lexicographic method and makes use of a single SAT solver that is preserved throughout the entire search procedure. It allows for solving three tasks for bi-objective optimization: finding a single Pareto-optimal solution, finding one representative solution for each Pareto point, and enumerating all Pareto-optimal solutions. We provide an open-source implementation of five variants of BiOptSat, building on different algorithms proposed for MaxSAT. Additionally, we empirically evaluate these five variants, comparing their runtime performance to that of three key competing algorithmic approaches. The empirical comparison in the contexts of learning interpretable decision rules and bi-objective set covering shows practical benefits of our approach. Furthermore, for the best-performing variant of BiOptSat, we study the effects of proposed refinements to determine their effectiveness

Parallel memetic algorithms for the problem of workforce distribution in dynamis multi-agent system

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 20/09/2013Esta tesis describe un novedoso enfoque para resolver el problema de distribución de carga de trabajo en sistemas multi-agente dinámicos basados en arquitecturas de pizarra, enfocándose especialmente en un escenario real: el call center multitarea. Para abordar este tipo de entornos dinámicos, tradicionalmente se han aplicado diversas heurísticas voraces que permiten dar una solución en tiempo real. Básicamente, dichas heurísticas realizan planificaciones continuamente, considerando el estado del sistema en cada momento. Como las decisiones se toman de forma voraz sin hacer una planificación óptima, la distribución de la carga de trabajo puede ser pobre a medio y/o largo plazo. El uso de algoritmos meméticos paralelos nos puede permitir encontrar soluciones mucho más precisas. Para aplicar este tipo de algoritmos, introducimos el concepto de ventana temporal adaptativa. De esta forma, el tamaño de la ventana temporal depende del nivel de dinamismo del sistema en un instante dado. Este trabajo propone una serie de herramientas para determinar el dinamismo del sistema de forma automática, así como un novedoso módulo de predicción basado en una red neuronal y un potente método de búsqueda basado en meta-algoritmos meméticos paralelos para poder lidiar con entornos dinámicos complejos. Para concluir, comparamos nuestro enfoque con otras técnicas del estado del arte en un entorno de producción real (Telefónica) obteniendo mejores resultados que el resto de técnicas actuales. También se proporciona un estudio exhaustivo de cada uno de los módulos.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Fahrplanbasiertes Energiemanagement in Smart Grids

Die Zunahme dezentraler, volatiler Stromerzeugung im Rahmen der Energiewende führt schon heute zu Engpässen in Stromnetzen. Eine Lösung dieser Probleme verspricht die informationstechnische Vernetzung und Koordination der Erzeuger und Verbraucher in Smart Grids. Diese Arbeit präsentiert einen Energiemanagement-Ansatz, der basierend auf Leistungsprognosen und Flexibilitäten der Akteure spezifische, aggregierte Leistungsprofile approximiert. Hierbei werden Netzrestriktionen berücksichtigt

Fahrplanbasiertes Energiemanagement in Smart Grids

Die Zunahme dezentraler, volatiler Stromerzeugung im Rahmen der Energiewende führt schon heute zu Engpässen in Stromnetzen. Eine Lösung dieser Probleme verspricht die informationstechnische Vernetzung und Koordination der Erzeuger und Verbraucher in Smart Grids. Diese Arbeit präsentiert einen Energiemanagement-Ansatz, der basierend auf Leistungsprognosen und Flexibilitäten der Akteure spezifische, aggregierte Leistungsprofile approximiert. Hierbei werden Netzrestriktionen berücksichtigt

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp