25,818 research outputs found
A 2-level Metaheuristic for the Set Covering Problem
Metaheuristics are solution methods which combine local improvement procedures and higher level strategies for solving combinatorial and nonlinear optimization problems. In general, metaheuristics require an important amount of effort focused on parameter setting to improve its performance. In this work a 2-level metaheuristic approach is proposed so that Scatter Search and Ant Colony Optimization act as âlow level" metaheuristics, whose parameters are set by a âhigher level" Genetic Algorithm during execution, seeking to improve the performance and to reduce the maintenance. The Set Covering Problem is taken as reference since is one of the most important optimization problems, serving as basis for facility location problems, airline crew scheduling, nurse scheduling, and resource allocation
Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results
The vast majority of Ant Colony Optimization (ACO) algorithms for inducing classification rules use an ACO-based procedure to create a rule in an one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-MinerPB algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules)-i.e., the ACO search is guided by the quality of a list of rules, instead of an individual rule. In this paper we propose an extension of the cAnt-MinerPB algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly-used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines and the cAnt-MinerPB producing ordered rules are also presented
Brain Modularity Mediates the Relation between Task Complexity and Performance
Recent work in cognitive neuroscience has focused on analyzing the brain as a
network, rather than as a collection of independent regions. Prior studies
taking this approach have found that individual differences in the degree of
modularity of the brain network relate to performance on cognitive tasks.
However, inconsistent results concerning the direction of this relationship
have been obtained, with some tasks showing better performance as modularity
increases and other tasks showing worse performance. A recent theoretical model
(Chen & Deem, 2015) suggests that these inconsistencies may be explained on the
grounds that high-modularity networks favor performance on simple tasks whereas
low-modularity networks favor performance on more complex tasks. The current
study tests these predictions by relating modularity from resting-state fMRI to
performance on a set of simple and complex behavioral tasks. Complex and simple
tasks were defined on the basis of whether they did or did not draw on
executive attention. Consistent with predictions, we found a negative
correlation between individuals' modularity and their performance on a
composite measure combining scores from the complex tasks but a positive
correlation with performance on a composite measure combining scores from the
simple tasks. These results and theory presented here provide a framework for
linking measures of whole brain organization from network neuroscience to
cognitive processing.Comment: 47 pages; 4 figure
Finding groups in data: Cluster analysis with ants
Wepresent in this paper a modification of Lumer and Faietaâs algorithm for data clustering. This approach
mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically
clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus
on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on
the final clustering by using during the classification differentmetrics of dissimilarity: Euclidean, Cosine,
and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more
conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired techniques, ant
clustering algorithms have received special attention, especially because they still require much
investigation to improve performance, stability and other key features that would make such algorithms
mature tools for data mining.
As a case study, this paper focus on the behavior of clustering procedures in those new approaches.
The proposed algorithm and its modifications are evaluated in a number of well-known benchmark
datasets. Empirical results clearly show that ant-based clustering algorithms performs well when
compared to another techniques
An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks
Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs
Approximations from Anywhere and General Rough Sets
Not all approximations arise from information systems. The problem of fitting
approximations, subjected to some rules (and related data), to information
systems in a rough scheme of things is known as the \emph{inverse problem}. The
inverse problem is more general than the duality (or abstract representation)
problems and was introduced by the present author in her earlier papers. From
the practical perspective, a few (as opposed to one) theoretical frameworks may
be suitable for formulating the problem itself. \emph{Granular operator spaces}
have been recently introduced and investigated by the present author in her
recent work in the context of antichain based and dialectical semantics for
general rough sets. The nature of the inverse problem is examined from
number-theoretic and combinatorial perspectives in a higher order variant of
granular operator spaces and some necessary conditions are proved. The results
and the novel approach would be useful in a number of unsupervised and semi
supervised learning contexts and algorithms.Comment: 20 Pages. Scheduled to appear in IJCRS'2017 LNCS Proceedings,
Springe
An (MI)LP-based Primal Heuristic for 3-Architecture Connected Facility Location in Urban Access Network Design
We investigate the 3-architecture Connected Facility Location Problem arising
in the design of urban telecommunication access networks. We propose an
original optimization model for the problem that includes additional variables
and constraints to take into account wireless signal coverage. Since the
problem can prove challenging even for modern state-of-the art optimization
solvers, we propose to solve it by an original primal heuristic which combines
a probabilistic fixing procedure, guided by peculiar Linear Programming
relaxations, with an exact MIP heuristic, based on a very large neighborhood
search. Computational experiments on a set of realistic instances show that our
heuristic can find solutions associated with much lower optimality gaps than a
state-of-the-art solver.Comment: This is the authors' final version of the paper published in:
Squillero G., Burelli P. (eds), EvoApplications 2016: Applications of
Evolutionary Computation, LNCS 9597, pp. 283-298, 2016. DOI:
10.1007/978-3-319-31204-0_19. The final publication is available at Springer
via http://dx.doi.org/10.1007/978-3-319-31204-0_1
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