Parallel metaheuristics: recent advances and new trends
The field of parallel metaheuristics is continuously evolving as a result of new technologies
and needs that researchers have been encountering. In the last decade, new models of …
and needs that researchers have been encountering. In the last decade, new models of …
Genetic algorithms, a nature-inspired tool: a survey of applications in materials science and related fields: part II
W Paszkowicz - Materials and Manufacturing Processes, 2013 - Taylor & Francis
Genetic algorithms (GAs) are a helpful tool in optimization, simulation, modelling, design,
and prediction purposes in various domains of science including materials science …
and prediction purposes in various domains of science including materials science …
Parallel genetic algorithm on the cuda architecture
P Pospichal, J Jaros, J Schwarz - European conference on the …, 2010 - Springer
This paper deals with the mapping of the parallel island-based genetic algorithm with
unidirectional ring migrations to nVidia CUDA software model. The proposed mapping is …
unidirectional ring migrations to nVidia CUDA software model. The proposed mapping is …
Large scale data mining using genetics-based machine learning
J Bacardit, X Llorà - Proceedings of the 11th Annual Conference …, 2009 - dl.acm.org
We are living in the peta-byte era. We have larger and larger data to analyze, process and
transform into useful answers for the domain experts. Robust data mining tools, able to cope …
transform into useful answers for the domain experts. Robust data mining tools, able to cope …
[HTML][HTML] An efficient automated parameter tuning framework for spiking neural networks
As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the
enormous number of open parameters in these models becomes a difficult challenge. SNNs …
enormous number of open parameters in these models becomes a difficult challenge. SNNs …
Parallel distributed hybrid fuzzy GBML models with rule set migration and training data rotation
H Ishibuchi, S Mihara, Y Nojima - IEEE Transactions on fuzzy …, 2012 - ieeexplore.ieee.org
We propose a parallel distributed model of a hybrid fuzzy genetics-based machine learning
(GBML) algorithm to drastically decrease its computation time. Our hybrid algorithm has a …
(GBML) algorithm to drastically decrease its computation time. Our hybrid algorithm has a …
Graphics processing units and genetic programming: an overview
WB Langdon - Soft computing, 2011 - Springer
A top end graphics card (GPU) plus a suitable SIMD interpreter can deliver a several
hundred fold speed up, yet cost less than the computer holding it. We give highlights of AI …
hundred fold speed up, yet cost less than the computer holding it. We give highlights of AI …
A new efficient radix sort
A Andersson, S Nilsson - Proceedings 35th Annual Symposium …, 1994 - ieeexplore.ieee.org
We present new improved algorithms for the sorting problem. The algorithms are not only
efficient but also clear and simple. First, we introduce Forward Radix Sort which combines …
efficient but also clear and simple. First, we introduce Forward Radix Sort which combines …
Speeding up the evaluation phase of GP classification algorithms on GPUs
The efficiency of evolutionary algorithms has become a studied problem since it is one of the
major weaknesses in these algorithms. Specifically, when these algorithms are employed for …
major weaknesses in these algorithms. Specifically, when these algorithms are employed for …
Parallelization of binary and real-coded genetic algorithms on GPU using CUDA
Genetic Algorithms (GAs) are suitable for parallel computing since population members
fitness maybe evaluated in parallel. Most past parallel GA studies have exploited this aspect …
fitness maybe evaluated in parallel. Most past parallel GA studies have exploited this aspect …