26,064 research outputs found
Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm
This paper combines the idea of a hierarchical distributed genetic algorithm
with different inter-agent partnering strategies. Cascading clusters of
sub-populations are built from bottom up, with higher-level sub-populations
optimising larger parts of the problem. Hence higher-level sub-populations
search a larger search space with a lower resolution whilst lower-level
sub-populations search a smaller search space with a higher resolution. The
effects of different partner selection schemes for (sub-)fitness evaluation
purposes are examined for two multiple-choice optimisation problems. It is
shown that random partnering strategies perform best by providing better
sampling and more diversity
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
The convolutional neural network (CNN), which is one of the deep learning
models, has seen much success in a variety of computer vision tasks. However,
designing CNN architectures still requires expert knowledge and a lot of trial
and error. In this paper, we attempt to automatically construct CNN
architectures for an image classification task based on Cartesian genetic
programming (CGP). In our method, we adopt highly functional modules, such as
convolutional blocks and tensor concatenation, as the node functions in CGP.
The CNN structure and connectivity represented by the CGP encoding method are
optimized to maximize the validation accuracy. To evaluate the proposed method,
we constructed a CNN architecture for the image classification task with the
CIFAR-10 dataset. The experimental result shows that the proposed method can be
used to automatically find the competitive CNN architecture compared with
state-of-the-art models.Comment: This is the revised version of the GECCO 2017 paper. The code of our
method is available at https://github.com/sg-nm/cgp-cn
Exploiting Tournament Selection for Efficient Parallel Genetic Programming
Genetic Programming (GP) is a computationally intensive technique which is
naturally parallel in nature. Consequently, many attempts have been made to
improve its run-time from exploiting highly parallel hardware such as GPUs.
However, a second methodology of improving the speed of GP is through
efficiency techniques such as subtree caching. However achieving parallel
performance and efficiency is a difficult task. This paper will demonstrate an
efficiency saving for GP compatible with the harnessing of parallel CPU
hardware by exploiting tournament selection. Significant efficiency savings are
demonstrated whilst retaining the capability of a high performance parallel
implementation of GP. Indeed, a 74% improvement in the speed of GP is achieved
with a peak rate of 96 billion GPop/s for classification type problems
'On the Application of Hierarchical Coevolutionary Genetic Algorithms: Recombination and Evaluation Partners'
This paper examines the use of a hierarchical coevolutionary genetic algorithm under different partnering strategies. Cascading clusters of sub-populations are built from the bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations potentially search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the sub-populations on solution quality are examined for two constrained optimisation problems. We examine a number of recombination partnering strategies in the construction of higher-level individuals and a number of related schemes for evaluating sub-solutions. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements
Evolutionary-based sparse regression for the experimental identification of duffing oscillator
In this paper, an evolutionary-based sparse regression algorithm is proposed and applied onto experimental data collected from a Duffing oscillator setup and numerical simulation data. Our purpose is to identify the Coulomb friction terms as part of the ordinary differential equation of the system. Correct identification of this nonlinear system using sparse identification is hugely dependent on selecting the correct form of nonlinearity included in the function library. Consequently, in this work, the evolutionary-based sparse identification is replacing the need for user knowledge when constructing the library in sparse identification. Constructing the library based on the data-driven evolutionary approach is an effective way to extend the space of nonlinear functions, allowing for the sparse regression to be applied on an extensive space of functions. The results show that the method provides an effective algorithm for the purpose of unveiling the physical nature of the Duffing oscillator. In addition, the robustness of the identification algorithm is investigated for various levels of noise in simulation. The proposed method has possible applications to other nonlinear dynamic systems in mechatronics, robotics, and electronics
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