1,814 research outputs found
On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling
Evolutionary algorithms have been frequently used for dynamic optimization
problems. With this paper, we contribute to the theoretical understanding of
this research area. We present the first computational complexity analysis of
evolutionary algorithms for a dynamic variant of a classical combinatorial
optimization problem, namely makespan scheduling. We study the model of a
strong adversary which is allowed to change one job at regular intervals.
Furthermore, we investigate the setting of random changes. Our results show
that randomized local search and a simple evolutionary algorithm are very
effective in dynamically tracking changes made to the problem instance.Comment: Conference version appears at IJCAI 201
Optimal location of car wreck adjusters
Su origen es difÃcil de determinar debido a las diversas restauraciones que se han llevado a cabo, los datos más antiguos son del siglo XVIII, aunque su origen es sin duda anterior. Junto a la Capilla se habilitó un hospital y asilo de transeúntes.
Presenta planta rectangular, de una sola nave cubierta con una artesa de yeso y el presbiterio con bóveda de arista. Tiene una espadaña con campana y veleta de forja. En la puerta podemos encontrar un azulejo polÃcromo del siglo XVIII con la imagen de la Virgen de los Remedios.
En el Interior, en el altar mayor, observamos una imagen de la Virgen de los Remedios, titular de la Capilla y Patrona del pueblo desde 1964. En el presbiterio, dentro de dos retablos neoclásicos, se encuentran las imágenes de San Isidro Labrador y de la Divina Pastora.
Desde comienzos del siglo XXI la capilla ha estado cerrada ya que presentaba un elevado estado de deterioro. Debido a esto las imágenes fueron trasladadas a la Iglesia Parroquial. En el año 2012, dicho edificio fue restaurado quedando nuevamente abierto al público
The Sampling-and-Learning Framework: A Statistical View of Evolutionary Algorithms
Evolutionary algorithms (EAs), a large class of general purpose optimization
algorithms inspired from the natural phenomena, are widely used in various
industrial optimizations and often show excellent performance. This paper
presents an attempt towards revealing their general power from a statistical
view of EAs. By summarizing a large range of EAs into the sampling-and-learning
framework, we show that the framework directly admits a general analysis on the
probable-absolute-approximate (PAA) query complexity. We particularly focus on
the framework with the learning subroutine being restricted as a binary
classification, which results in the sampling-and-classification (SAC)
algorithms. With the help of the learning theory, we obtain a general upper
bound on the PAA query complexity of SAC algorithms. We further compare SAC
algorithms with the uniform search in different situations. Under the
error-target independence condition, we show that SAC algorithms can achieve
polynomial speedup to the uniform search, but not super-polynomial speedup.
Under the one-side-error condition, we show that super-polynomial speedup can
be achieved. This work only touches the surface of the framework. Its power
under other conditions is still open
Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
Plateaus can be harder in multi-objective optimization
AbstractIn recent years a lot of progress has been made in understanding the behavior of evolutionary computation methods for single- and multi-objective problems. Our aim is to analyze the diversity mechanisms that are implicitly used in evolutionary algorithms for multi-objective problems by rigorous runtime analyses. We show that, even if the population size is small, the runtime can be exponential where corresponding single-objective problems are optimized within polynomial time. To illustrate this behavior we analyze a simple plateau function in a first step and extend our result to a class of instances of the well-known SetCover problem
Algorithm Engineering in Robust Optimization
Robust optimization is a young and emerging field of research having received
a considerable increase of interest over the last decade. In this paper, we
argue that the the algorithm engineering methodology fits very well to the
field of robust optimization and yields a rewarding new perspective on both the
current state of research and open research directions.
To this end we go through the algorithm engineering cycle of design and
analysis of concepts, development and implementation of algorithms, and
theoretical and experimental evaluation. We show that many ideas of algorithm
engineering have already been applied in publications on robust optimization.
Most work on robust optimization is devoted to analysis of the concepts and the
development of algorithms, some papers deal with the evaluation of a particular
concept in case studies, and work on comparison of concepts just starts. What
is still a drawback in many papers on robustness is the missing link to include
the results of the experiments again in the design
A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem
The 0-1 knapsack problem is a well-known combinatorial optimisation problem.
Approximation algorithms have been designed for solving it and they return
provably good solutions within polynomial time. On the other hand, genetic
algorithms are well suited for solving the knapsack problem and they find
reasonably good solutions quickly. A naturally arising question is whether
genetic algorithms are able to find solutions as good as approximation
algorithms do. This paper presents a novel multi-objective optimisation genetic
algorithm for solving the 0-1 knapsack problem. Experiment results show that
the new algorithm outperforms its rivals, the greedy algorithm, mixed strategy
genetic algorithm, and greedy algorithm + mixed strategy genetic algorithm
Bilevel models on the competitive facility location problem
Facility location and allocation problems have been a major area of research for decades, which has led to a vast and still growing literature. Although there are many variants of these problems, there exist two common features: finding the best locations for one or more facilities and allocating demand points to these facilities. A considerable number of studies assume a monopolistic viewpoint and formulate a mathematical model to optimize an objective function of a single decision maker. In contrast, competitive facility location (CFL) problem is based on the premise that there exist competition in the market among different firms. When one of the competing firms acts as the leader and the other firm, called the follower, reacts to the decision of the leader, a sequential-entry CFL problem is obtained, which gives rise to a Stackelberg type of game between two players. A successful and widely applied framework to formulate this type of CFL problems is bilevel programming (BP). In this chapter, the literature on BP models for CFL problems is reviewed, existing works are categorized with respect to defined criteria, and information is provided for each work.WOS:000418225000002Scopus - Affiliation ID: 60105072Book Citation Index- Science - Book Citation Index- Social Sciences and HumanitiesArticle; Book ChapterOcak2017YÖK - 2016-1
- …