66 research outputs found
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
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
Towards Superinstructions for Java Interpreters
The Java Virtual Machine (JVM) is usually implemented by an interpreter or just-in-time (JIT) compiler. JITs provide the best performance, but interpreters have a number of advantages that make them attractive, especially for embedded systems. These advantages include simplicity, portability and lower memory requirements. Instruction dispatch is responsible for most of the running time of efficient interpreters, especially on pipelined processors. Superinstructions are an important optimisation to reduce the number of instruction dispatches. A superinstruction is a new Java instruction which performs the work of a common sequence of instructions. In this paper we describe work in progress on the design and implementation of a system of superinstructions for an efficient Java interpreter for connected devices and embedded systems. We describe our basic interpreter, the interpreter generator we use to automatically create optimised source code for superinstructions, and discuss Java specific issues relating to superinstructions. Our initial experimental results show that superinstructions can give large speedups on the SPECjvm98 benchmark suite
Parallelization Strategies for Ant Colony Optimisation on GPUs
Ant Colony Optimisation (ACO) is an effective population-based meta-heuristic
for the solution of a wide variety of problems. As a population-based
algorithm, its computation is intrinsically massively parallel, and it is
there- fore theoretically well-suited for implementation on Graphics Processing
Units (GPUs). The ACO algorithm comprises two main stages: Tour construction
and Pheromone update. The former has been previously implemented on the GPU,
using a task-based parallelism approach. However, up until now, the latter has
always been implemented on the CPU. In this paper, we discuss several
parallelisation strategies for both stages of the ACO algorithm on the GPU. We
propose an alternative data-based parallelism scheme for Tour construction,
which fits better on the GPU architecture. We also describe novel GPU
programming strategies for the Pheromone update stage. Our results show a total
speed-up exceeding 28x for the Tour construction stage, and 20x for Pheromone
update, and suggest that ACO is a potentially fruitful area for future research
in the GPU domain.Comment: Accepted by 14th International Workshop on Nature Inspired
Distributed Computing (NIDISC 2011), held in conjunction with the 25th
IEEE/ACM International Parallel and Distributed Processing Symposium (IPDPS
2011
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