94 research outputs found
Regularity Model for Noisy Multiobjective Optimization
Regularity models have been used in dealing with noise-free multiobjective optimization problems. This paper studies the behavior of a regularity model in noisy environments and argues that it is very suitable for noisy multiobjective optimization. We propose to embed the regularity model in an existing multiobjective evolutionary algorithm for tackling noises. The proposed algorithm works well in terms of both convergence and diversity. In our experimental studies, we have compared several state-of-the-art of algorithms with our proposed algorithm on benchmark problems with different levels of noises. The experimental results showed the effectiveness of the regularity model on noisy problems, but a degenerated performance on some noisy-free problems
Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes
Network structure evolves with time in the real world, and the discovery of
changing communities in dynamic networks is an important research topic that
poses challenging tasks. Most existing methods assume that no significant
change in the network occurs; namely, the difference between adjacent snapshots
is slight. However, great change exists in the real world usually. The great
change in the network will result in the community detection algorithms are
difficulty obtaining valuable information from the previous snapshot, leading
to negative transfer for the next time steps. This paper focuses on dynamic
community detection with substantial changes by integrating higher-order
knowledge from the previous snapshots to aid the subsequent snapshots.
Moreover, to improve search efficiency, a higher-order knowledge transfer
strategy is designed to determine first-order and higher-order knowledge by
detecting the similarity of the adjacency matrix of snapshots. In this way, our
proposal can better keep the advantages of previous community detection results
and transfer them to the next task. We conduct the experiments on four
real-world networks, including the networks with great or minor changes.
Experimental results in the low-similarity datasets demonstrate that
higher-order knowledge is more valuable than first-order knowledge when the
network changes significantly and keeps the advantage even if handling the
high-similarity datasets. Our proposal can also guide other dynamic
optimization problems with great changes.Comment: Submitted to IEEE TEV
Objective reduction based on nonlinear correlation information entropy
© 2015, Springer-Verlag Berlin Heidelberg.It is hard to obtain the entire solution set of a many-objective optimization problem (MaOP) by multi-objective evolutionary algorithms (MOEAs) because of the difficulties brought by the large number of objectives. However, the redundancy of objectives exists in some problems with correlated objectives (linearly or nonlinearly). Objective reduction can be used to decrease the difficulties of some MaOPs. In this paper, we propose a novel objective reduction approach based on nonlinear correlation information entropy (NCIE). It uses the NCIE matrix to measure the linear and nonlinear correlation between objectives and a simple method to select the most conflicting objectives during the execution of MOEAs. We embed our approach into both Pareto-based and indicator-based MOEAs to analyze the impact of our reduction method on the performance of these algorithms. The results show that our approach significantly improves the performance of Pareto-based MOEAs on both reducible and irreducible MaOPs, but does not much help the performance of indicator-based MOEAs
B2Opt: Learning to Optimize Black-box Optimization with Little Budget
The core challenge of high-dimensional and expensive black-box optimization
(BBO) is how to obtain better performance faster with little function
evaluation cost. The essence of the problem is how to design an efficient
optimization strategy tailored to the target task. This paper designs a
powerful optimization framework to automatically learn the optimization
strategies from the target or cheap surrogate task without human intervention.
However, current methods are weak for this due to poor representation of
optimization strategy. To achieve this, 1) drawing on the mechanism of genetic
algorithm, we propose a deep neural network framework called B2Opt, which has a
stronger representation of optimization strategies based on survival of the
fittest; 2) B2Opt can utilize the cheap surrogate functions of the target task
to guide the design of the efficient optimization strategies. Compared to the
state-of-the-art BBO baselines, B2Opt can achieve multiple orders of magnitude
performance improvement with less function evaluation cost. We validate our
proposal on high-dimensional synthetic functions and two real-world
applications. We also find that deep B2Opt performs better than shallow ones
Pre-trained transformer for adversarial purification
With more and more deep neural networks being deployed as various daily
services, their reliability is essential. It's frightening that deep neural
networks are vulnerable and sensitive to adversarial attacks, the most common
one of which for the services is evasion-based. Recent works usually strengthen
the robustness by adversarial training or leveraging the knowledge of an amount
of clean data. However, in practical terms, retraining and redeploying the
model need a large computational budget, leading to heavy losses to the online
service. In addition, when adversarial examples of a certain attack are
detected, only limited adversarial examples are available for the service
provider, while much clean data may not be accessible. Given the mentioned
problems, we propose a new scenario, RaPiD (Rapid Plug-in Defender), which is
to rapidly defend against a certain attack for the frozen original service
model with limitations of few clean and adversarial examples. Motivated by the
generalization and the universal computation ability of pre-trained transformer
models, we come up with a new defender method, CeTaD, which stands for
Considering Pre-trained Transformers as Defenders. In particular, we evaluate
the effectiveness and the transferability of CeTaD in the case of one-shot
adversarial examples and explore the impact of different parts of CeTaD as well
as training data conditions. CeTaD is flexible, able to be embedded into an
arbitrary differentiable model, and suitable for various types of attacks
Diversity assessment in many-objective optimization
Maintaining diversity is one important aim of multiobjective optimization. However, diversity for many-objective optimization problems is less straightforward to define than for multi-objective optimization problems. Inspired by measures for biodiversity, we propose a new diversity metric for manyobjective optimization, which is an accumulation of the dissimilarity in the population, where an Lp-norm-based (p < 1) distance is adopted to measure the dissimilarity of solutions. Empirical results demonstrate our proposed metric can more accurately assess the diversity of solutions in various situations. We compare the diversity of the solutions obtained by four popular many-objective evolutionary algorithms using the proposed diversity metric on a large number of benchmark problems with two to ten objectives. The behaviors of different diversity maintenance methodologies in those algorithms are discussed in depth based on the experimental results. Finally, we show that the proposed diversity measure can also be employed for enhancing diversity maintenance or reference set generation in many-objective optimization
Maximizing geographical efficiency : An analysis of the configuration of Colorado’s trauma system
ACKNOWLEDGEMENT The data used for this study were supplied by the Health Facilities and Emergency Medical Services Division of the Colorado Department of Public Health and Environment, which specifically disclaims responsibility for any analyses, interpretations, or conclusions it has not provided. The data used for this study were supplied by the Health Facilities and Emergency Medical Services Division of the Colorado Department of Public Health and Environment, which specifically disclaims responsibility for any analyses, interpretations, or conclusions it has not provided.Peer reviewedPostprin
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