994 research outputs found
A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation
Evolutionary algorithms have been frequently applied to constrained
continuous optimisation problems. We carry out feature based comparisons of
different types of evolutionary algorithms such as evolution strategies,
differential evolution and particle swarm optimisation for constrained
continuous optimisation. In our study, we examine how sets of constraints
influence the difficulty of obtaining close to optimal solutions. Using a
multi-objective approach, we evolve constrained continuous problems having a
set of linear and/or quadratic constraints where the different evolutionary
approaches show a significant difference in performance. Afterwards, we discuss
the features of the constraints that exhibit a difference in performance of the
different evolutionary approaches under consideration.Comment: 16 Pagesm 2 Figure
Pippi - painless parsing, post-processing and plotting of posterior and likelihood samples
Interpreting samples from likelihood or posterior probability density
functions is rarely as straightforward as it seems it should be. Producing
publication-quality graphics of these distributions is often similarly painful.
In this short note I describe pippi, a simple, publicly-available package for
parsing and post-processing such samples, as well as generating high-quality
PDF graphics of the results. Pippi is easily and extensively configurable and
customisable, both in its options for parsing and post-processing samples, and
in the visual aspects of the figures it produces. I illustrate some of these
using an existing supersymmetric global fit, performed in the context of a
gamma-ray search for dark matter. Pippi can be downloaded and followed at
http://github.com/patscott/pippi .Comment: 4 pages, 1 figure. v3: Updated for pippi 2.0. New features include
hdf5 support, out-of-core processing, inline post-processing with arbitrary
Python code in the input file, and observable-specific data cuts. Pippi can
be downloaded from http://github.com/patscott/pipp
Experimental Comparisons of Derivative Free Optimization Algorithms
In this paper, the performances of the quasi-Newton BFGS algorithm, the
NEWUOA derivative free optimizer, the Covariance Matrix Adaptation Evolution
Strategy (CMA-ES), the Differential Evolution (DE) algorithm and Particle Swarm
Optimizers (PSO) are compared experimentally on benchmark functions reflecting
important challenges encountered in real-world optimization problems.
Dependence of the performances in the conditioning of the problem and
rotational invariance of the algorithms are in particular investigated.Comment: 8th International Symposium on Experimental Algorithms, Dortmund :
Germany (2009
A hybrid multiagent approach for global trajectory optimization
In this paper we consider a global optimization method for space trajectory design problems. The method, which actually aims at finding not only the global minimizer but a whole set of low-lying local minimizers(corresponding to a set of different design options), is based on a domain
decomposition technique where each subdomain is evaluated through a procedure based on the evolution of a population of agents. The method is applied to two space trajectory design problems and compared with existing deterministic and stochastic global optimization methods
Trace and detect adversarial attacks on CNNs using feature response maps
The existence of adversarial attacks on convolutional neural networks (CNN) questions the fitness of such models for serious applications. The attacks manipulate an input image such that misclassification is evoked while still looking normal to a human observer – they are thus not easily detectable. In a different context, backpropagated activations of CNN hidden layers – “feature responses” to a given input – have been helpful to visualize for a human “debugger” what the CNN “looks at” while computing its output. In this work, we propose a novel detection method for adversarial examples to prevent attacks. We do so by tracking adversarial perturbations in feature responses, allowing for automatic detection using average local spatial entropy. The method does not alter the original network architecture and is fully human-interpretable. Experiments confirm the validity of our approach for state-of-the-art attacks on large-scale models trained on ImageNet
Differential evolution for the offline and online optimization of fed-batch fermentation processes
The optimization of input variables (typically feeding trajectories over
time) in fed-batch fermentations has gained special attention, given the economic impact
and the complexity of the problem. Evolutionary Computation (EC) has been a
source of algorithms that have shown good performance in this task. In this chapter,
Differential Evolution (DE) is proposed to tackle this problem and quite promising
results are shown. DE is tested in several real world case studies and compared with
other EC algorihtms, such as Evolutionary Algorithms and Particle Swarms. Furthermore,
DE is also proposed as an alternative to perform online optimization, where the
input variables are adjusted while the real fermentation process is ongoing. In this case,
a changing landscape is optimized, therefore making the task of the algorithms more
difficult. However, that fact does not impair the performance of the DE and confirms
its good behaviour.(undefined
From feature selection to continuous optimization
Metaheuristic algorithms (MAs) have seen unprecedented growth thanks to their
successful applications in fields including engineering and health sciences. In
this work, we investigate the use of a deep learning (DL) model as an
alternative tool to do so. The proposed method, called MaNet, is motivated by
the fact that most of the DL models often need to solve massive nasty
optimization problems consisting of millions of parameters. Feature selection
is the main adopted concepts in MaNet that helps the algorithm to skip
irrelevant or partially relevant evolutionary information and uses those which
contribute most to the overall performance. The introduced model is applied on
several unimodal and multimodal continuous problems. The experiments indicate
that MaNet is able to yield competitive results compared to one of the best
hand-designed algorithms for the aforementioned problems, in terms of the
solution accuracy and scalability.Comment: Accepted for EA201
SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget
In the context of industrial engineering, it is important to integrate
efficient computational optimization methods in the product development
process. Some of the most challenging simulation-based engineering design
optimization problems are characterized by: a large number of design variables,
the absence of analytical gradients, highly non-linear objectives and a limited
function evaluation budget. Although a huge variety of different optimization
algorithms is available, the development and selection of efficient algorithms
for problems with these industrial relevant characteristics, remains a
challenge. In this communication, a hybrid variant of Differential Evolution
(DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG)
methods within the framework of DE, in order to improve optimization efficiency
on problems with the previously mentioned characteristics. The performance of
the resulting derivative-free algorithm is compared with other state-of-the-art
DE variants on 25 commonly used benchmark functions, under tight function
evaluation budget constraints of 1000 evaluations. The experimental results
indicate that the new algorithm performs excellent on the 'difficult' (high
dimensional, multi-modal, inseparable) test functions. The operations used in
the proposed mutation scheme, are computationally inexpensive, and can be
easily implemented in existing differential evolution variants or other
population-based optimization algorithms by a few lines of program code as an
non-invasive optional setting. Besides the applicability of the presented
algorithm by itself, the described concepts can serve as a useful and
interesting addition to the algorithmic operators in the frameworks of
heuristics and evolutionary optimization and computing
Selection models with monotone weight functions in meta analysis
Publication bias, the fact that studies identified for inclusion in a meta
analysis do not represent all studies on the topic of interest, is commonly
recognized as a threat to the validity of the results of a meta analysis. One
way to explicitly model publication bias is via selection models or weighted
probability distributions. We adopt the nonparametric approach initially
introduced by Dear (1992) but impose that the weight function is monotonely
non-increasing as a function of the -value. Since in meta analysis one
typically only has few studies or "observations", regularization of the
estimation problem seems sensible. In addition, virtually all parametric weight
functions proposed so far in the literature are in fact decreasing. We discuss
how to estimate a decreasing weight function in the above model and illustrate
the new methodology on two well-known examples. The new approach potentially
offers more insight in the selection process than other methods and is more
flexible than parametric approaches. Some basic properties of the
log-likelihood function and computation of a -value quantifying the evidence
against the null hypothesis of a constant weight function are indicated. In
addition, we provide an approximate selection bias adjusted profile likelihood
confidence interval for the treatment effect. The corresponding software and
the datasets used to illustrate it are provided as the R package selectMeta.
This enables full reproducibility of the results in this paper.Comment: 15 pages, 2 figures. Some minor changes according to reviewer
comment
Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms
Evolution-in-Materio uses evolutionary algorithms (EA) to exploit the physical properties of unconfigured, physically rich materials, in effect transforming them into information processors. The potential of this technique for machine learning problems is explored here. Results are obtained from a mixture of single walled carbon nanotubes and liquid crystals (SWCNT/LC). The complex nature of the voltage/current relationship of this material presents a potential for adaptation. Here, it is used as a computational medium evolved by two derivative-free, population-based stochastic search algorithms, particle swarm optimisation (PSO) and differential evolution (DE). The computational problem considered is data classification. A custom made electronic motherboard for interacting with the material has been developed, which allows the application of control signals on the material body. Starting with a simple binary classification problem of separable data, the material is trained with an error minimisation objective for both algorithms. Subsequently, the solution, defined as the combination of the material itself and optimal inputs, is verified and results are reported. The evolution process based on EAs has the capacity to evolve the material to a state where data classification can be performed. PSO outperforms DE in terms of results’ reproducibility due to the smoother, as opposed to more noisy, inputs applied on the material
- …