scholarly journals Choosing the Right Algorithm With Hints From Complexity Theory

2021 ◽  
Author(s):  
Shouda Wang ◽  
Weijie Zheng ◽  
Benjamin Doerr
Keyword(s):  
Complexity Theory ◽  
Optimization Problem ◽  
Broad Class ◽  
Black Box ◽  
Metropolis Algorithm ◽  
Search Heuristics ◽  
Interesting Candidate ◽  
Optimization Heuristics ◽  
Estimation Of Distribution ◽  
The Right

Choosing a suitable algorithm from the myriads of different search heuristics is difficult when faced with a novel optimization problem. In this work, we argue that the purely academic question of what could be the best possible algorithm in a certain broad class of black-box optimizers can give fruitful indications in which direction to search for good established optimization heuristics. We demonstrate this approach on the recently proposed DLB benchmark, for which the only known results are O(n^3) runtimes for several classic evolutionary algorithms and an O(n^2 log n) runtime for an estimation-of-distribution algorithm. Our finding that the unary unbiased black-box complexity is only O(n^2) suggests the Metropolis algorithm as an interesting candidate and we prove that it solves the DLB problem in quadratic time. Since we also prove that better runtimes cannot be obtained in the class of unary unbiased algorithms, we shift our attention to algorithms that use the information of more parents to generate new solutions. An artificial algorithm of this type having an O(n log n) runtime leads to the result that the significance-based compact genetic algorithm (sig-cGA) can solve the DLB problem also in time O(n log n). Our experiments show a remarkably good performance of the Metropolis algorithm, clearly the best of all algorithms regarded for reasonable problem sizes.

scholarly journals A New Ensemble Adversarial Attack Powered by Long-Term Gradient Memories

2020 ◽  
Vol 34 (04) ◽  
pp. 3405-3413
Author(s):  
Zhaohui Che ◽  
Ali Borji ◽  
Guangtao Zhai ◽  
Suiyi Ling ◽  
Jing Li ◽  
...  
Keyword(s):  
Broad Class ◽  
Black Box ◽  
Security Threat ◽  
Source Models ◽  
Adversarial Examples ◽  
Adversarial Attack ◽  
Prediction Systems ◽  
Attack And Defense ◽  
Decision Boundaries

Deep neural networks are vulnerable to adversarial attacks. More importantly, some adversarial examples crafted against an ensemble of pre-trained source models can transfer to other new target models, thus pose a security threat to black-box applications (when the attackers have no access to the target models). Despite adopting diverse architectures and parameters, source and target models often share similar decision boundaries. Therefore, if an adversary is capable of fooling several source models concurrently, it can potentially capture intrinsic transferable adversarial information that may allow it to fool a broad class of other black-box target models. Current ensemble attacks, however, only consider a limited number of source models to craft an adversary, and obtain poor transferability. In this paper, we propose a novel black-box attack, dubbed Serial-Mini-Batch-Ensemble-Attack (SMBEA). SMBEA divides a large number of pre-trained source models into several mini-batches. For each single batch, we design 3 new ensemble strategies to improve the intra-batch transferability. Besides, we propose a new algorithm that recursively accumulates the “long-term” gradient memories of the previous batch to the following batch. This way, the learned adversarial information can be preserved and the inter-batch transferability can be improved. Experiments indicate that our method outperforms state-of-the-art ensemble attacks over multiple pixel-to-pixel vision tasks including image translation and salient region prediction. Our method successfully fools two online black-box saliency prediction systems including DeepGaze-II (Kummerer 2017) and SALICON (Huang et al. 2017). Finally, we also contribute a new repository to promote the research on adversarial attack and defense over pixel-to-pixel tasks: https://github.com/CZHQuality/AAA-Pix2pix.

A Modified Binary Crow Search Algorithm for Solving the Graph Coloring Problem

2020 ◽  
Vol 11 (2) ◽  
pp. 28-46 ◽  
Author(s):  
Yassine Meraihi ◽  
Mohammed Mahseur ◽  
Dalila Acheli
Keyword(s):  
Graph Coloring ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Distribution Method ◽  
Local Optima ◽  
Coloring Problem ◽  
Graph Coloring Problem ◽  
Np Hard Problem ◽  
The Right

The graph coloring problem (GCP) is a well-known classical combinatorial optimization problem in graph theory. It is known to be an NP-Hard problem, so many heuristic algorithms have been employed to solve this problem. This article proposes a modified binary crow search algorithm (MBCSA) to solve the graph coloring problem. First, the binary crow search algorithm is obtained from the original crow search algorithm using the V-shaped transfer function and the discretization method. Second, we use chaotic maps to choose the right values of the flight length (FL) and the awareness probability (AP). Third, we adopt the Gaussian distribution method to replace the random variables used for updating the position of the crows. The aim of these contributions is to avoid the premature convergence to local optima and ensure the diversity of the solutions. To evaluate the performance of our algorithm, we use the well-known DIMACS benchmark graph coloring instances. The simulation results reveal the efficiency of our proposed algorithm in comparison with other existing algorithms in the literature.

Study shows psychological capital and work engagement mediate the impact of high-performance work systems (HPWS) on intentions to quit, creativity and extra-role performance

2019 ◽  
Vol 27 (7) ◽  
pp. 7-8
Keyword(s):  
Work Engagement ◽  
High Performance ◽  
Psychological Capital ◽  
Black Box ◽  
Role Performance ◽  
Employee Outcomes ◽  
Customer Contact ◽  
Content Type ◽  
The Right ◽  
The Impact

Purpose The researchers wanted to examine the mediating factors operating in the “black box” between HPWS and employee outcomes. Design/methodology/approach The authors obtained their data from a variety of customer-contact employees, such as front desk agents, food servers and concierges, in four and five-star hotels in the Romanian cities of Sibiu and Bucharest. They sent out questionnaires measuring psychological capital, such as self-reliance, hope, resilience and optimism, as well as work engagement, creative performance and extra-role performance. Supervisors were also asked their opinions. Findings The analysis of customer-contact employees and their supervisors in the Romanian hotel industry suggested that psychological capital and work engagement were the two most important factors operating in the “black box” between HPWS and employee outcomes. Originality/value There is great value for businesses in the conclusions of the research. It shows how critical it is to establish various HPWS programs that boost engagement, as well as indicating the importance of providing job security and designing recruitment processes that root out people with the right skills.

Introducing Elitist Black-Box Models: When Does Elitist Behavior Weaken the Performance of Evolutionary Algorithms?

2017 ◽  
Vol 25 (4) ◽  
pp. 587-606 ◽  
Author(s):  
Carola Doerr ◽  
Johannes Lengler
Keyword(s):  
Monte Carlo ◽  
Evolutionary Algorithms ◽  
Function Class ◽  
Black Box ◽  
Fixed Number ◽  
Exponential Factor ◽  
Expected Time ◽  
Box Models ◽  
Search Heuristics ◽  
Black Box Models

Black-box complexity theory provides lower bounds for the runtime of black-box optimizers like evolutionary algorithms and other search heuristics and serves as an inspiration for the design of new genetic algorithms. Several black-box models covering different classes of algorithms exist, each highlighting a different aspect of the algorithms under considerations. In this work we add to the existing black-box notions a new elitist black-box model, in which algorithms are required to base all decisions solely on (the relative performance of) a fixed number of the best search points sampled so far. Our elitist model thus combines features of the ranking-based and the memory-restricted black-box models with an enforced usage of truncation selection. We provide several examples for which the elitist black-box complexity is exponentially larger than that of the respective complexities in all previous black-box models, thus showing that the elitist black-box complexity can be much closer to the runtime of typical evolutionary algorithms. We also introduce the concept of p-Monte Carlo black-box complexity, which measures the time it takes to optimize a problem with failure probability at most p. Even for small  p, the p-Monte Carlo black-box complexity of a function class [Formula: see text] can be smaller by an exponential factor than its typically regarded Las Vegas complexity (which measures the expected time it takes to optimize [Formula: see text]).

Teeth-Number Synthesis of an Automatic Planetary Transmission Using Evolutionary Computation

2004 ◽  
Author(s):  
P. A. Simionescu ◽  
D. G. Beale ◽  
G. V. Dozier
Keyword(s):  
Constrained Optimization ◽  
Objective Function ◽  
Evolutionary Computation ◽  
Optimization Problem ◽  
Design Parameters ◽  
Constrained Optimization Problem ◽  
Gear Teeth ◽  
Estimation Of Distribution ◽  
Number Synthesis ◽  
Planetary Transmission

The gear-teeth number synthesis of an automatic planetary transmission used in automobiles is formulated as a constrained optimization problem that is solved with the aid of an Estimation of Distribution Algorithm. The design parameters are the teeth number of each gear, the number of multiple planets and gear module, while the objective function is defined based on the departure between the imposed and the actual gear ratios, constrained by teeth-undercut avoidance, limiting the maximum overall diameter of the transmission and ensuring proper planet spacing.

scholarly journals Constructing banking networks under decreasing costs of link formation

2021 ◽  
Author(s):  
Dietmar Maringer ◽  
Ben Craig ◽  
Sandra Paterlini
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
Balance Sheet ◽  
Financial Networks ◽  
Fixed Costs ◽  
Balance Sheets ◽  
Search Heuristics ◽  
Banking Networks ◽  
Link Formation ◽  
Do So

AbstractThe structure of networks plays a central role in the behavior of financial systems and their response to policy. Real-world networks, however, are rarely directly observable: banks’ assets and liabilities are typically known, but not who is lending how much and to whom. This paper adds to the existing literature in two ways. First, it shows how to simulate realistic networks that are based on balance-sheet information. To do so, we introduce a model where links cause fixed-costs, independent of contract size; but the costs per link decrease the more connected a bank is (scale economies). Second, to approach the optimization problem, we develop a new algorithm inspired by the transportation planning literature and research in stochastic search heuristics. Computational experiments find that the resulting networks are not only consistent with the balance sheets, but also resemble real-world financial networks in their density (which is sparse but not minimally dense) and in their core-periphery and disassortative structure.

Toward an optimal performance index for neurosurgical robot's design

Robotica ◽  
2009 ◽  
Vol 28 (2) ◽  
pp. 279-296 ◽  
Author(s):  
Alessandro Gasparetto ◽  
Vanni Zanotto
Keyword(s):  
Optimization Problem ◽  
Target Position ◽  
Point Of View ◽  
Performance Indices ◽  
The Past ◽  
Surgical Devices ◽  
Definition Of ◽  
The Right ◽  
Kinematic Point ◽  
Suitable System

SUMMARYIn the past years a large number of new surgical devices have been developed to improve the operation outcomes and reduce the patient's trauma. Nevertheless, the dexterity and accuracy required in positioning the surgical tools are often unreachable if the surgeons are not assisted by a suitable system. Since a medical robot works in an operating room, close to the patient and the medical staff, it has to satisfy much stricter requirements with respect to an industrial one. From a kinematic point of view, the robot must reach any target position in the patient's body, being as less invasive as possible for the surgeon's workspace. In order to meet such requirements, the right robot structure has to be chosen by means of the definition of suitable kinematic performance indices.In this paper some task-based indices based on the robot workspace and stiffness are presented and discussed. The indices will be used in a multiobjective optimization problem to evaluate best robot kinematic structure for a given neurosurgical task.

scholarly journals High-Stiffness, Lock-and-Key Heat-Reversible Locator-Snap Systems for the Design for Disassembly

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Mohammed Shalaby ◽  
Kazuhiro Saitou
Keyword(s):  
Optimization Problem ◽  
Screw Theory ◽  
Design Stage ◽  
Design For Disassembly ◽  
Element Analysis ◽  
Material Recycling ◽  
High Stiffness ◽  
Multi Objective Genetic Algorithm ◽  
Trade Offs ◽  
The Right

Driven by the moral sense of obligation, legislative and social pressures, manufacturers now consider effective part reuse and material recycling at the end of product life at the design stage. It is a key consideration to use joints that can disengage with minimum labor, part damage, and material contamination. This paper extends our previous work on the design of high-stiffness reversible locator-snap system that can disengage nondestructively with localized heat (Shalaby and Saitou, 2006, “Optimal Heat-Reversible Snap Joints for Frame-Panel Assembly in Aluminum Space Frame Automotive Bodies,” Proceedings of the LCE2006: The 13th CIRP International Conference on Life Cycle Engineering, Leuven, Belgium, May 31–Jun. 2, pp. 411–416; Shalaby and Saitou, 2008, “Design for Disassembly With High-Stiffness, Heat-Reversible Locator-Snap Systems,” ASME J. Mech. Des., 130(12), p. 121701) to include (1) modeling for tolerance stack-up and (2) lock-and-key concept to ensure that snaps only disengage when the right procedure is followed. The design problem is posed as an optimization problem to find the locations, numbers, and orientations of locators and snaps, and the locations and sizes of heating areas, to release the snaps with minimum heat, compliance, and tolerance stack-up. The motion and structural requirements are considered constraints. Screw theory is employed to precalculate the set of feasible types and orientations of locators and snaps that are examined during optimization. Multi-objective genetic algorithm coupled with structural and thermal finite element analysis is used to solve the optimization problem. The method is applied on two case studies. The Pareto-optimal solutions present alternative designs with different trade-offs between the design objectives.

Sign in / Sign up

Export Citation Format

Share Document