35,023 research outputs found
Cooperation of Nature and Physiologically Inspired Mechanism in Visualisation
A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect (al-Rifaie, Bishop & Blackwell, 2011). We also discuss whether or not the ‘art works’ generated by nature and biologically inspired algorithms can possibly be considered as ‘computationally creative’
D4M 3.0: Extended Database and Language Capabilities
The D4M tool was developed to address many of today's data needs. This tool
is used by hundreds of researchers to perform complex analytics on unstructured
data. Over the past few years, the D4M toolbox has evolved to support
connectivity with a variety of new database engines, including SciDB.
D4M-Graphulo provides the ability to do graph analytics in the Apache Accumulo
database. Finally, an implementation using the Julia programming language is
also now available. In this article, we describe some of our latest additions
to the D4M toolbox and our upcoming D4M 3.0 release. We show through
benchmarking and scaling results that we can achieve fast SciDB ingest using
the D4M-SciDB connector, that using Graphulo can enable graph algorithms on
scales that can be memory limited, and that the Julia implementation of D4M
achieves comparable performance or exceeds that of the existing MATLAB(R)
implementation.Comment: IEEE HPEC 201
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
Multitask Learning is a learning paradigm that deals with multiple different
tasks in parallel and transfers knowledge among them. XOF, a Learning
Classifier System using tree-based programs to encode building blocks
(meta-features), constructs and collects features with rich discriminative
information for classification tasks in an observed list. This paper seeks to
facilitate the automation of feature transferring in between tasks by utilising
the observed list. We hypothesise that the best discriminative features of a
classification task carry its characteristics. Therefore, the relatedness
between any two tasks can be estimated by comparing their most appropriate
patterns. We propose a multiple-XOF system, called mXOF, that can dynamically
adapt feature transfer among XOFs. This system utilises the observed list to
estimate the task relatedness. This method enables the automation of
transferring features. In terms of knowledge discovery, the resemblance
estimation provides insightful relations among multiple data. We experimented
mXOF on various scenarios, e.g. representative Hierarchical Boolean problems,
classification of distinct classes in the UCI Zoo dataset, and unrelated tasks,
to validate its abilities of automatic knowledge-transfer and estimating task
relatedness. Results show that mXOF can estimate the relatedness reasonably
between multiple tasks to aid the learning performance with the dynamic feature
transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference
(GECCO 2020
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