scholarly journals Noisy Optimization With Evolution Strategies

2002 ◽  
Author(s):  
Dirk V. Arnold
Keyword(s):  
Evolution Strategies ◽  
Noisy Optimization

On the Benefits of Populations for Noisy Optimization

2003 ◽  
Vol 11 (2) ◽  
pp. 111-127 ◽  
Author(s):  
Dirk V. Arnold ◽  
Hans-Georg Beyer
Keyword(s):  
Order Statistics ◽  
Dynamic Process ◽  
Evolutionary Dynamics ◽  
Optimization Problems ◽  
Evolution Strategies ◽  
High Dimensional ◽  
Noisy Optimization ◽  
Search Spaces ◽  
Improved Performance ◽  
Positive Effect

It is known that, in the absence of noise, no improvement in local performance can be gained from retaining candidate solutions other than the best one. Yet, it has been shown experimentally that, in the presence of noise, operating with a non-singular population of candidate solutions can have a marked and positive effect on the local performance of evolution strategies. So as to determine the reasons for the improved performance, we have studied the evolutionary dynamics of the (μ, λ)-ES in the presence of noise. Considering a simple, idealized environment, we have developed a moment-based approach that uses recent results involving concomitants of selected order statistics. This approach yields an intuitive explanation for the performance advantage of multi-parent strategies in the presence of noise. It is then shown that the idealized dynamic process considered does bear relevance to optimization problems in high-dimensional search spaces.

Noisy Optimization by Evolution Strategies With Online Population Size Learning

2021 ◽  
pp. 1-13
Author(s):  
Zhenhua Li ◽  
Shuo Zhang ◽  
Xinye Cai ◽  
Qingfu Zhang ◽  
Xiaomin Zhu ◽  
...  
Keyword(s):  
Population Size ◽  
Evolution Strategies ◽  
Noisy Optimization

scholarly journals Noisy Optimization of Dispatching Policy for the Cranes at the Storage Yard in an Automated Container Terminal

2021 ◽  
Vol 11 (15) ◽  
pp. 6922
Author(s):  
Jeongmin Kim ◽  
Ellen J. Hong ◽  
Youngjee Yang ◽  
Kwang Ryel Ryu
Keyword(s):  
Container Terminal ◽  
Scoring Function ◽  
Weight Vector ◽  
Container Terminals ◽  
Exploration And Exploitation ◽  
Noisy Optimization ◽  
Automated Container Terminal ◽  
Simulation Based ◽  
The Cost ◽  
Operation Schedule

In this paper, we claim that the operation schedule of automated stacking cranes (ASC) in the storage yard of automated container terminals can be built effectively and efficiently by using a crane dispatching policy, and propose a noisy optimization algorithm named N-RTS that can derive such a policy efficiently. To select a job for an ASC, our dispatching policy uses a multi-criteria scoring function to calculate the score of each candidate job using a weighted summation of the evaluations in those criteria. As the calculated score depends on the respective weights of these criteria, and thus a different weight vector gives rise to a different best candidate, a weight vector can be deemed as a policy. A good weight vector, or policy, can be found by a simulation-based search where a candidate policy is evaluated through a computationally expensive simulation of applying the policy to some operation scenarios. We may simplify the simulation to save time but at the cost of sacrificing the evaluation accuracy. N-RTS copes with this dilemma by maintaining a good balance between exploration and exploitation. Experimental results show that the policy derived by N-RTS outperforms other ASC scheduling methods. We also conducted additional experiments using some benchmark functions to validate the performance of N-RTS.

scholarly journals Evolution-Strategies-Driven Optimization on Secure and Reconfigurable Interconnection PUF Networks

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 537
Author(s):  
Hongxiang Gu ◽  
Miodrag Potkonjak
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Evolution Strategies ◽  
Light Weight ◽  
Network Configuration ◽  
System Complexity ◽  
Physical Unclonable Functions ◽  
Output Stability ◽  
Output Mapping ◽  
Weight Design

Physical Unclonable Functions (PUFs) are known for their unclonability and light-weight design. However, several known issues with state-of-the-art PUF designs exist including vulnerability against machine learning attacks, low output randomness, and low reliability. To address these problems, we present a reconfigurable interconnected PUF network (IPN) design that significantly strengthens the security and unclonability of strong PUFs. While the IPN structure itself significantly increases the system complexity and nonlinearity, the reconfiguration mechanism remaps the input–output mapping before an attacker could collect sufficient challenge-response pairs (CRPs). We also propose using an evolution strategies (ES) algorithm to efficiently search for a network configuration that is capable of producing random and stable responses. The experimental results show that applying state-of-the-art machine learning attacks result in less than 53.19% accuracy for single-bit output prediction on a reconfigurable IPN with random configurations. We also show that, when applying configurations explored by our proposed ES method instead of random configurations, the output randomness is significantly improved by 220.8% and output stability by at least 22.62% in different variations of IPN.

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