18,182 research outputs found
Making Racing Fun Through Player Modeling and Track Evolution
This paper addresses the problem of automatically constructing tracks tailor-made to maximize the enjoyment of individual players in a simple car racing game. To this end, some approaches to player modeling are investigated, and a method of using evolutionary algorithms to construct racing tracks is presented. A simple player-dependent metric of entertainment is proposed and used as the fitness function when evolving tracks. We conclude that accurate player modeling poses some significant challenges, but track evolution works well given the right track representation
Photonic Structures Optimization Using Highly Data-Efficient Deep Learning: Application To Nanofin And Annular Groove Phase Masks
Metasurfaces offer a flexible framework for the manipulation of light
properties in the realm of thin film optics. Specifically, the polarization of
light can be effectively controlled through the use of thin phase plates. This
study aims to introduce a surrogate optimization framework for these devices.
The framework is applied to develop two kinds of vortex phase masks (VPMs)
tailored for application in astronomical high-contrast imaging. Computational
intelligence techniques are exploited to optimize the geometric features of
these devices. The large design space and computational limitations necessitate
the use of surrogate models like partial least squares Kriging, radial basis
functions, or neural networks. However, we demonstrate the inadequacy of these
methods in modeling the performance of VPMs. To address the shortcomings of
these methods, a data-efficient evolutionary optimization setup using a deep
neural network as a highly accurate and efficient surrogate model is proposed.
The optimization process in this study employs a robust particle swarm
evolutionary optimization scheme, which operates on explicit geometric
parameters of the photonic device. Through this approach, optimal designs are
developed for two design candidates. In the most complex case, evolutionary
optimization enables optimization of the design that would otherwise be
impractical (requiring too much simulations). In both cases, the surrogate
model improves the reliability and efficiency of the procedure, effectively
reducing the required number of simulations by up to 75% compared to
conventional optimization techniques
Automated Circuit Approximation Method Driven by Data Distribution
We propose an application-tailored data-driven fully automated method for
functional approximation of combinational circuits. We demonstrate how an
application-level error metric such as the classification accuracy can be
translated to a component-level error metric needed for an efficient and fast
search in the space of approximate low-level components that are used in the
application. This is possible by employing a weighted mean error distance
(WMED) metric for steering the circuit approximation process which is conducted
by means of genetic programming. WMED introduces a set of weights (calculated
from the data distribution measured on a selected signal in a given
application) determining the importance of each input vector for the
approximation process. The method is evaluated using synthetic benchmarks and
application-specific approximate MAC (multiply-and-accumulate) units that are
designed to provide the best trade-offs between the classification accuracy and
power consumption of two image classifiers based on neural networks.Comment: Accepted for publication at Design, Automation and Test in Europe
(DATE 2019). Florence, Ital
Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction
In this paper we develop a novel neural network model for predicting implied
volatility surface. Prior financial domain knowledge is taken into account. A
new activation function that incorporates volatility smile is proposed, which
is used for the hidden nodes that process the underlying asset price. In
addition, financial conditions, such as the absence of arbitrage, the
boundaries and the asymptotic slope, are embedded into the loss function. This
is one of the very first studies which discuss a methodological framework that
incorporates prior financial domain knowledge into neural network architecture
design and model training. The proposed model outperforms the benchmarked
models with the option data on the S&P 500 index over 20 years. More
importantly, the domain knowledge is satisfied empirically, showing the model
is consistent with the existing financial theories and conditions related to
implied volatility surface.Comment: 8 pages, SIGKDD 202
Genetic Programming for Smart Phone Personalisation
Personalisation in smart phones requires adaptability to dynamic context
based on user mobility, application usage and sensor inputs. Current
personalisation approaches, which rely on static logic that is developed a
priori, do not provide sufficient adaptability to dynamic and unexpected
context. This paper proposes genetic programming (GP), which can evolve program
logic in realtime, as an online learning method to deal with the highly dynamic
context in smart phone personalisation. We introduce the concept of
collaborative smart phone personalisation through the GP Island Model, in order
to exploit shared context among co-located phone users and reduce convergence
time. We implement these concepts on real smartphones to demonstrate the
capability of personalisation through GP and to explore the benefits of the
Island Model. Our empirical evaluations on two example applications confirm
that the Island Model can reduce convergence time by up to two-thirds over
standalone GP personalisation.Comment: 43 pages, 11 figure
Application of artificial neural network in market segmentation: A review on recent trends
Despite the significance of Artificial Neural Network (ANN) algorithm to
market segmentation, there is a need of a comprehensive literature review and a
classification system for it towards identification of future trend of market
segmentation research. The present work is the first identifiable academic
literature review of the application of neural network based techniques to
segmentation. Our study has provided an academic database of literature between
the periods of 2000-2010 and proposed a classification scheme for the articles.
One thousands (1000) articles have been identified, and around 100 relevant
selected articles have been subsequently reviewed and classified based on the
major focus of each paper. Findings of this study indicated that the research
area of ANN based applications are receiving most research attention and self
organizing map based applications are second in position to be used in
segmentation. The commonly used models for market segmentation are data mining,
intelligent system etc. Our analysis furnishes a roadmap to guide future
research and aid knowledge accretion and establishment pertaining to the
application of ANN based techniques in market segmentation. Thus the present
work will significantly contribute to both the industry and academic research
in business and marketing as a sustainable valuable knowledge source of market
segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table
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