4,112 research outputs found
Multiorder neurons for evolutionary higher-order clustering and growth
This letter proposes to use multiorder neurons for clustering irregularly shaped data arrangements. Multiorder neurons are an evolutionary extension of the use of higher-order neurons in clustering. Higher-order neurons parametrically model complex neuron shapes by replacing the classic synaptic weight by higher-order tensors. The multiorder neuron goes one step further and eliminates two problems associated with higher-order neurons. First, it uses evolutionary algorithms to select the best neuron order for a given problem. Second, it obtains more information about the underlying data distribution by identifying the correct order for a given cluster of patterns. Empirically we observed that when the correlation of clusters found with ground truth information is used in measuring clustering accuracy, the proposed evolutionary multiorder neurons method can be shown to outperform other related clustering methods. The simulation results from the Iris, Wine, and Glass data sets show significant improvement when compared to the results obtained using self-organizing maps and higher-order neurons. The letter also proposes an intuitive model by which multiorder neurons can be grown, thereby determining the number of clusters in data
Behavioral Learning of Aircraft Landing Sequencing Using a Society of Probabilistic Finite State Machines
Air Traffic Control (ATC) is a complex safety critical environment. A tower
controller would be making many decisions in real-time to sequence aircraft.
While some optimization tools exist to help the controller in some airports,
even in these situations, the real sequence of the aircraft adopted by the
controller is significantly different from the one proposed by the optimization
algorithm. This is due to the very dynamic nature of the environment. The
objective of this paper is to test the hypothesis that one can learn from the
sequence adopted by the controller some strategies that can act as heuristics
in decision support tools for aircraft sequencing. This aim is tested in this
paper by attempting to learn sequences generated from a well-known sequencing
method that is being used in the real world. The approach relies on a genetic
algorithm (GA) to learn these sequences using a society Probabilistic
Finite-state Machines (PFSMs). Each PFSM learns a different sub-space; thus,
decomposing the learning problem into a group of agents that need to work
together to learn the overall problem. Three sequence metrics (Levenshtein,
Hamming and Position distances) are compared as the fitness functions in GA. As
the results suggest, it is possible to learn the behavior of the
algorithm/heuristic that generated the original sequence from very limited
information
Genetic algorithms
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology
Sparse experimental design : an effective an efficient way discovering better genetic algorithm structures
The focus of this paper is the demonstration that sparse experimental design is a useful strategy for developing Genetic Algorithms. It is increasingly apparent from a number of reports and papers within a variety of different problem domains that the 'best' structure for a GA may be dependent upon the application. The GA structure is defined as both the types of operators and the parameters settings used during operation. The differences observed may be linked to the nature of the problem, the type of fitness function, or the depth or breadth of the problem under investigation. This paper demonstrates that advanced experimental design may be adopted to increase the understanding of the relationships between the GA structure and the problem domain, facilitating the selection of improved structures with a minimum of effort
Combining chromosomal arm status and significantly aberrant genomic locations reveals new cancer subtypes
Many types of tumors exhibit chromosomal losses or gains, as well as local
amplifications and deletions. Within any given tumor type, sample specific
amplifications and deletionsare also observed. Typically, a region that is
aberrant in more tumors,or whose copy number change is stronger, would be
considered as a more promising candidate to be biologically relevant to cancer.
We sought for an intuitive method to define such aberrations and prioritize
them. We define V, the volume associated with an aberration, as the product of
three factors: a. fraction of patients with the aberration, b. the aberrations
length and c. its amplitude. Our algorithm compares the values of V derived
from real data to a null distribution obtained by permutations, and yields the
statistical significance, p value, of the measured value of V. We detected
genetic locations that were significantly aberrant and combined them with
chromosomal arm status to create a succint fingerprint of the tumor genome.
This genomic fingerprint is used to visualize the tumors, highlighting events
that are co ocurring or mutually exclusive. We allpy the method on three
different public array CGH datasets of Medulloblastoma and Neuroblastoma, and
demonstrate its ability to detect chromosomal regions that were known to be
altered in the tested cancer types, as well as to suggest new genomic locations
to be tested. We identified a potential new subtype of Medulloblastoma, which
is analogous to Neuroblastoma type 1.Comment: 34 pages, 3 figures; to appear in Cancer Informatic
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