28,585 research outputs found
Fitness landscape of the cellular automata majority problem: View from the Olympus
In this paper we study cellular automata (CAs) that perform the computational
Majority task. This task is a good example of what the phenomenon of emergence
in complex systems is. We take an interest in the reasons that make this
particular fitness landscape a difficult one. The first goal is to study the
landscape as such, and thus it is ideally independent from the actual
heuristics used to search the space. However, a second goal is to understand
the features a good search technique for this particular problem space should
possess. We statistically quantify in various ways the degree of difficulty of
searching this landscape. Due to neutrality, investigations based on sampling
techniques on the whole landscape are difficult to conduct. So, we go exploring
the landscape from the top. Although it has been proved that no CA can perform
the task perfectly, several efficient CAs for this task have been found.
Exploiting similarities between these CAs and symmetries in the landscape, we
define the Olympus landscape which is regarded as the ''heavenly home'' of the
best local optima known (blok). Then we measure several properties of this
subspace. Although it is easier to find relevant CAs in this subspace than in
the overall landscape, there are structural reasons that prevent a searcher
from finding overfitted CAs in the Olympus. Finally, we study dynamics and
performance of genetic algorithms on the Olympus in order to confirm our
analysis and to find efficient CAs for the Majority problem with low
computational cost
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The management of intelligence-assisted finite element analysis technology
Artificial Intelligence (AI) approaches to Finite Element Analysis (FEA), have had tentative degrees of success over the last few years and some authors have argued that effective FEA can help in the manufacture reliability and safety aspects of engineered artefacts. The author of this paper reviews how such AI techniques have been applied and in this light, the author then uses a Fuzzy Cognitive Mapping (FCM), to develop a framework for the management of intelligence-assisted FEA
A Composite Likelihood-based Approach for Change-point Detection in Spatio-temporal Process
This paper develops a unified, accurate and computationally efficient method
for change-point inference in non-stationary spatio-temporal processes. By
modeling a non-stationary spatio-temporal process as a piecewise stationary
spatio-temporal process, we consider simultaneous estimation of the number and
locations of change-points, and model parameters in each segment. A composite
likelihood-based criterion is developed for change-point and parameters
estimation. Asymptotic theories including consistency and distribution of the
estimators are derived under mild conditions. In contrast to classical results
in fixed dimensional time series that the asymptotic error of change-point
estimator is , exact recovery of true change-points is guaranteed in
the spatio-temporal setting. More surprisingly, the consistency of change-point
estimation can be achieved without any penalty term in the criterion function.
A computational efficient pruned dynamic programming algorithm is developed for
the challenging criterion optimization problem. Simulation studies and an
application to U.S. precipitation data are provided to demonstrate the
effectiveness and practicality of the proposed method
Controls and guidance research
The objectives of the control group are concentrated on research and education. The control problem of the hypersonic space vehicle represents an important and challenging issue in aerospace engineering. The work described in this report is part of our effort in developing advanced control strategies for such a system. In order to achieve the objectives stated in the NASA-CORE proposal, the tasks were divided among the group based upon their educational expertise. Within the educational component we are offering a Linear Systems and Control course for students in electrical and mechanical engineering. Also, we are proposing a new course in Digital Control Systems with a corresponding laboratory
Inductive machine learning of optimal modular structures: Estimating solutions using support vector machines
Structural optimization is usually handled by iterative methods requiring repeated samples of a physics-based model, but this process can be computationally demanding. Given a set of previously optimized structures of the same topology, this paper uses inductive learning to replace this optimization process entirely by deriving a function that directly maps any given load to an optimal geometry. A support vector machine is trained to determine the optimal geometry of individual modules of a space frame structure given a specified load condition. Structures produced by learning are compared against those found by a standard gradient descent optimization, both as individual modules and then as a composite structure. The primary motivation for this is speed, and results show the process is highly efficient for cases in which similar optimizations must be performed repeatedly. The function learned by the algorithm can approximate the result of optimization very closely after sufficient training, and has also been found effective at generalizing the underlying optima to produce structures that perform better than those found by standard iterative methods
Quantitative Genetics and Functional-Structural Plant Growth Models: Simulation of Quantitative Trait Loci Detection for Model Parameters and Application to Potential Yield Optimization
Background and Aims: Prediction of phenotypic traits from new genotypes under
untested environmental conditions is crucial to build simulations of breeding
strategies to improve target traits. Although the plant response to
environmental stresses is characterized by both architectural and functional
plasticity, recent attempts to integrate biological knowledge into genetics
models have mainly concerned specific physiological processes or crop models
without architecture, and thus may prove limited when studying genotype x
environment interactions. Consequently, this paper presents a simulation study
introducing genetics into a functional-structural growth model, which gives
access to more fundamental traits for quantitative trait loci (QTL) detection
and thus to promising tools for yield optimization. Methods: The GreenLab model
was selected as a reasonable choice to link growth model parameters to QTL.
Virtual genes and virtual chromosomes were defined to build a simple genetic
model that drove the settings of the species-specific parameters of the model.
The QTL Cartographer software was used to study QTL detection of simulated
plant traits. A genetic algorithm was implemented to define the ideotype for
yield maximization based on the model parameters and the associated allelic
combination. Key Results and Conclusions: By keeping the environmental factors
constant and using a virtual population with a large number of individuals
generated by a Mendelian genetic model, results for an ideal case could be
simulated. Virtual QTL detection was compared in the case of phenotypic traits
- such as cob weight - and when traits were model parameters, and was found to
be more accurate in the latter case. The practical interest of this approach is
illustrated by calculating the parameters (and the corresponding genotype)
associated with yield optimization of a GreenLab maize model. The paper
discusses the potentials of GreenLab to represent environment x genotype
interactions, in particular through its main state variable, the ratio of
biomass supply over demand
Denoising Autoencoders for fast Combinatorial Black Box Optimization
Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Autoencoders (AE) are
generative stochastic networks with these desired properties. We integrate a
special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate
the performance of DAE-EDA on several combinatorial optimization problems with
a single objective. We asses the number of fitness evaluations as well as the
required CPU times. We compare the results to the performance to the Bayesian
Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a
generative neural network which has proven competitive with BOA. For the
considered problem instances, DAE-EDA is considerably faster than BOA and
RBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is
higher than for BOA, but competitive with RBM-EDA. These results show that DAEs
can be useful tools for problems with low but non-negligible fitness evaluation
costs.Comment: corrected typos and small inconsistencie
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