516 research outputs found
Swarm-Based Spatial Sorting
Purpose: To present an algorithm for spatially sorting objects into an
annular structure. Design/Methodology/Approach: A swarm-based model that
requires only stochastic agent behaviour coupled with a pheromone-inspired
"attraction-repulsion" mechanism. Findings: The algorithm consistently
generates high-quality annular structures, and is particularly powerful in
situations where the initial configuration of objects is similar to those
observed in nature. Research limitations/implications: Experimental evidence
supports previous theoretical arguments about the nature and mechanism of
spatial sorting by insects. Practical implications: The algorithm may find
applications in distributed robotics. Originality/value: The model offers a
powerful minimal algorithmic framework, and also sheds further light on the
nature of attraction-repulsion algorithms and underlying natural processes.Comment: Accepted by the Int. J. Intelligent Computing and Cybernetic
NanoInfoBio: A case-study in interdisciplinary research
A significant amount of high-impact contemporary scientific research occurs
where biology, computer science, engineering and chemistry converge. Although
programmes have been put in place to support such work, the complex dynamics of
interdisciplinarity are still poorly understood. In this paper we highlight
potential barriers to effective research across disciplines, and suggest, using
a case study, possible mechanisms for removing these impediments.Comment: Appears in Kettunen, J., Hyrkkanen, U. & Lehto, A. (Eds.) Applied
Research and Professional Education, p.p. 289-309. Turku University of
Applied Sciences (2012). http://julkaisut.turkuamk.fi/isbn9789522162519.pdf.
arXiv admin note: substantial text overlap with arXiv:1012.417
A population-based microbial oscillator
Genetic oscillators are a major theme of interest in the emerging field of
synthetic biology. Until recently, most work has been carried out using
intra-cellular oscillators, but this approach restricts the broader
applicability of such systems. Motivated by a desire to develop large-scale,
spatially-distributed cell-based computational systems, we present an initial
design for a population-level oscillator which uses three different bacterial
strains. Our system is based on the client-server model familiar to computer
science, and uses quorum sensing for communication between nodes. We present
the results of extensive in silico simulation tests, which confirm that our
design is both feasible and robust.Comment: Submitte
Fitness Landscape-Based Characterisation of Nature-Inspired Algorithms
A significant challenge in nature-inspired algorithmics is the identification
of specific characteristics of problems that make them harder (or easier) to
solve using specific methods. The hope is that, by identifying these
characteristics, we may more easily predict which algorithms are best-suited to
problems sharing certain features. Here, we approach this problem using fitness
landscape analysis. Techniques already exist for measuring the "difficulty" of
specific landscapes, but these are often designed solely with evolutionary
algorithms in mind, and are generally specific to discrete optimisation. In
this paper we develop an approach for comparing a wide range of continuous
optimisation algorithms. Using a fitness landscape generation technique, we
compare six different nature-inspired algorithms and identify which methods
perform best on landscapes exhibiting specific features.Comment: 10 pages, 1 figure, submitted to the 11th International Conference on
Adaptive and Natural Computing Algorithm
Zen Puzzle Garden is NP-complete
Zen Puzzle Garden (ZPG) is a one-player puzzle game. In this paper, we prove
that deciding the solvability of ZPG is NP-complete.Comment: Submitte
Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms
The problem of parameterization is often central to the effective deployment
of nature-inspired algorithms. However, finding the optimal set of parameter
values for a combination of problem instance and solution method is highly
challenging, and few concrete guidelines exist on how and when such tuning may
be performed. Previous work tends to either focus on a specific algorithm or
use benchmark problems, and both of these restrictions limit the applicability
of any findings. Here, we examine a number of different algorithms, and study
them in a "problem agnostic" fashion (i.e., one that is not tied to specific
instances) by considering their performance on fitness landscapes with varying
characteristics. Using this approach, we make a number of observations on which
algorithms may (or may not) benefit from tuning, and in which specific
circumstances.Comment: 8 pages, 7 figures. Accepted at the European Conference on Artificial
Life (ECAL) 2013, Taormina, Ital
- …