3,732 research outputs found
An Effective Machine-Part Grouping Algorithm to Construct Manufacturing Cells
The machine-part cell formation problem consists of creating machine cells
and their corresponding part families with the objective of minimizing the
inter-cell and intra-cell movement while maximizing the machine utilization.
This article demonstrates a hybrid clustering approach for the cell formation
problem in cellular manufacturing that conjoins Sorenson s similarity
coefficient based method to form the production cells. Computational results
are shown over the test datasets obtained from the past literature. The hybrid
technique is shown to outperform the other methods proposed in literature and
including powerful soft computing approaches such as genetic algorithms,
genetic programming by exceeding the solution quality on the test problems
Distance-Based Bias in Model-Directed Optimization of Additively Decomposable Problems
For many optimization problems it is possible to define a distance metric
between problem variables that correlates with the likelihood and strength of
interactions between the variables. For example, one may define a metric so
that the dependencies between variables that are closer to each other with
respect to the metric are expected to be stronger than the dependencies between
variables that are further apart. The purpose of this paper is to describe a
method that combines such a problem-specific distance metric with information
mined from probabilistic models obtained in previous runs of estimation of
distribution algorithms with the goal of solving future problem instances of
similar type with increased speed, accuracy and reliability. While the focus of
the paper is on additively decomposable problems and the hierarchical Bayesian
optimization algorithm, it should be straightforward to generalize the approach
to other model-directed optimization techniques and other problem classes.
Compared to other techniques for learning from experience put forward in the
past, the proposed technique is both more practical and more broadly
applicable
Semi-supervised clustering methods
Cluster analysis methods seek to partition a data set into homogeneous
subgroups. It is useful in a wide variety of applications, including document
processing and modern genetics. Conventional clustering methods are
unsupervised, meaning that there is no outcome variable nor is anything known
about the relationship between the observations in the data set. In many
situations, however, information about the clusters is available in addition to
the values of the features. For example, the cluster labels of some
observations may be known, or certain observations may be known to belong to
the same cluster. In other cases, one may wish to identify clusters that are
associated with a particular outcome variable. This review describes several
clustering algorithms (known as "semi-supervised clustering" methods) that can
be applied in these situations. The majority of these methods are modifications
of the popular k-means clustering method, and several of them will be described
in detail. A brief description of some other semi-supervised clustering
algorithms is also provided.Comment: 28 pages, 5 figure
Scalability of Genetic Programming and Probabilistic Incremental Program Evolution
This paper discusses scalability of standard genetic programming (GP) and the
probabilistic incremental program evolution (PIPE). To investigate the need for
both effective mixing and linkage learning, two test problems are considered:
ORDER problem, which is rather easy for any recombination-based GP, and TRAP or
the deceptive trap problem, which requires the algorithm to learn interactions
among subsets of terminals. The scalability results show that both GP and PIPE
scale up polynomially with problem size on the simple ORDER problem, but they
both scale up exponentially on the deceptive problem. This indicates that while
standard recombination is sufficient when no interactions need to be
considered, for some problems linkage learning is necessary. These results are
in agreement with the lessons learned in the domain of binary-string genetic
algorithms (GAs). Furthermore, the paper investigates the effects of
introducing utnnecessary and irrelevant primitives on the performance of GP and
PIPE.Comment: Submitted to GECCO-200
Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOA
An automated technique has recently been proposed to transfer learning in the
hierarchical Bayesian optimization algorithm (hBOA) based on distance-based
statistics. The technique enables practitioners to improve hBOA efficiency by
collecting statistics from probabilistic models obtained in previous hBOA runs
and using the obtained statistics to bias future hBOA runs on similar problems.
The purpose of this paper is threefold: (1) test the technique on several
classes of NP-complete problems, including MAXSAT, spin glasses and minimum
vertex cover; (2) demonstrate that the technique is effective even when
previous runs were done on problems of different size; (3) provide empirical
evidence that combining transfer learning with other efficiency enhancement
techniques can often yield nearly multiplicative speedups.Comment: Accepted at Parallel Problem Solving from Nature (PPSN XII), 10
pages. arXiv admin note: substantial text overlap with arXiv:1201.224
Theoretical Perspective of Convergence Complexity of Evolutionary Algorithms Adopting Optimal Mixing
The optimal mixing evolutionary algorithms (OMEAs) have recently drawn much
attention for their robustness, small size of required population, and
efficiency in terms of number of function evaluations (NFE). In this paper, the
performances and behaviors of OMEAs are studied by investigating the mechanism
of optimal mixing (OM), the variation operator in OMEAs, under two scenarios --
one-layer and two-layer masks. For the case of one-layer masks, the required
population size is derived from the viewpoint of initial supply, while the
convergence time is derived by analyzing the progress of sub-solution growth.
NFE is then asymptotically bounded with rational probability by estimating the
probability of performing evaluations. For the case of two-layer masks,
empirical results indicate that the required population size is proportional to
both the degree of cross competition and the results from the one-layer-mask
case. The derived models also indicate that population sizing is decided by
initial supply when disjoint masks are adopted, that the high selection
pressure imposed by OM makes the composition of sub-problems impact little on
NFE, and that the population size requirement for two-layer masks increases
with the reverse-growth probability.Comment: 8 pages, 2015 GECCO oral pape
A Multifactorial Optimization Paradigm for Linkage Tree Genetic Algorithm
Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algorithm
(EA) to solve complex problems using the linkage information between problem
variables. LTGA performs well in various kinds of single-task optimization and
yields promising results in comparison with the canonical genetic algorithm.
However, LTGA is an unsuitable method for dealing with multi-task optimization
problems. On the other hand, Multifactorial Optimization (MFO) can
simultaneously solve independent optimization problems, which are encoded in a
unified representation to take advantage of the process of knowledge transfer.
In this paper, we introduce Multifactorial Linkage Tree Genetic Algorithm
(MF-LTGA) by combining the main features of both LTGA and MFO. MF-LTGA is able
to tackle multiple optimization tasks at the same time, each task learns the
dependency between problem variables from the shared representation. This
knowledge serves to determine the high-quality partial solutions for supporting
other tasks in exploring the search space. Moreover, MF-LTGA speeds up
convergence because of knowledge transfer of relevant problems. We demonstrate
the effectiveness of the proposed algorithm on two benchmark problems:
Clustered Shortest-Path Tree Problem and Deceptive Trap Function. In comparison
to LTGA and existing methods, MF-LTGA outperforms in quality of the solution or
in computation time
Statistical Properties of the Single Linkage Hierarchical Clustering Estimator
Distance-based hierarchical clustering (HC) methods are widely used in
unsupervised data analysis but few authors take account of uncertainty in the
distance data. We incorporate a statistical model of the uncertainty through
corruption or noise in the pairwise distances and investigate the problem of
estimating the HC as unknown parameters from measurements. Specifically, we
focus on single linkage hierarchical clustering (SLHC) and study its geometry.
We prove that under fairly reasonable conditions on the probability
distribution governing measurements, SLHC is equivalent to maximum partial
profile likelihood estimation (MPPLE) with some of the information contained in
the data ignored. At the same time, we show that direct evaluation of SLHC on
maximum likelihood estimation (MLE) of pairwise distances yields a consistent
estimator. Consequently, a full MLE is expected to perform better than SLHC in
getting the correct HC results for the ground truth metric.Comment: 21 pages, 6 figure
Gene Expression Data Knowledge Discovery using Global and Local Clustering
To understand complex biological systems, the research community has produced
huge corpus of gene expression data. A large number of clustering approaches
have been proposed for the analysis of gene expression data. However,
extracting important biological knowledge is still harder. To address this
task, clustering techniques are used. In this paper, hybrid Hierarchical
k-Means algorithm is used for clustering and biclustering gene expression data
is used. To discover both local and global clustering structure biclustering
and clustering algorithms are utilized. A validation technique, Figure of Merit
is used to determine the quality of clustering results. Appropriate knowledge
is mined from the clusters by embedding a BLAST similarity search program into
the clustering and biclustering process. To discover both local and global
clustering structure biclustering and clustering algorithms are utilized. To
determine the quality of clustering results, a validation technique, Figure of
Merit is used. Appropriate knowledge is mined from the clusters by embedding a
BLAST similarity search program into the clustering and biclustering process
Hierarchical D ∗ algorithm with materialization of costs for robot path planning
In this paper a new hierarchical extension of the D
∗ algorithm for robot path planning is introduced. The hierarchical D
∗
algorithm uses a down-top strategy and a set of precalculated paths (materialization of path costs) in order to improve performance.
This on-line path planning algorithm allows optimality and specially lower computational time. H-Graphs (hierarchical graphs)
are modified and adapted to support on-line path planning with materialization of costs and multiple hierarchical levels. Traditional
on-line robot path planning focused in horizontal spaces is also extended to vertical and interbuilding spaces. Some experimental
results are showed and compared to other path planning algorithms
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