scholarly journals Algorithm Portfolios for Noisy Optimization: Compare Solvers Early

2014 ◽  
pp. 1-15 ◽  
Author(s):  
Marie-Liesse Cauwet ◽  
Jialin Liu ◽  
Olivier Teytaud
Keyword(s):  
Noisy Optimization ◽  
Algorithm Portfolios

scholarly journals Algorithm portfolios for noisy optimization

2015 ◽  
Vol 76 (1-2) ◽  
pp. 143-172 ◽  
Author(s):  
Marie-Liesse Cauwet ◽  
Jialin Liu ◽  
Baptiste Rozière ◽  
Olivier Teytaud
Keyword(s):  
Noisy Optimization ◽  
Algorithm Portfolios

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.

SNOBFIT -- Stable Noisy Optimization by Branch and Fit

2008 ◽  
Vol 35 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Waltraud Huyer ◽  
Arnold Neumaier
Keyword(s):  
Noisy Optimization

scholarly journals On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments

2018 ◽  
Vol 26 (2) ◽  
pp. 237-267 ◽  
Author(s):  
Chao Qian ◽  
Yang Yu ◽  
Ke Tang ◽  
Yaochu Jin ◽  
Xin Yao ◽  
...  
Keyword(s):  
Negative Impact ◽  
Fitness Function ◽  
Evolutionary Optimization ◽  
Function Evaluation ◽  
Noisy Optimization ◽  
Running Time ◽  
Analysis And Design ◽  
True Value ◽  
Wide Range ◽  
The Impact

In real-world optimization tasks, the objective (i.e., fitness) function evaluation is often disturbed by noise due to a wide range of uncertainties. Evolutionary algorithms are often employed in noisy optimization, where reducing the negative effect of noise is a crucial issue. Sampling is a popular strategy for dealing with noise: to estimate the fitness of a solution, it evaluates the fitness multiple ([Formula: see text]) times independently and then uses the sample average to approximate the true fitness. Obviously, sampling can make the fitness estimation closer to the true value, but also increases the estimation cost. Previous studies mainly focused on empirical analysis and design of efficient sampling strategies, while the impact of sampling is unclear from a theoretical viewpoint. In this article, we show that sampling can speed up noisy evolutionary optimization exponentially via rigorous running time analysis. For the (1[Formula: see text]1)-EA solving the OneMax and the LeadingOnes problems under prior (e.g., one-bit) or posterior (e.g., additive Gaussian) noise, we prove that, under a high noise level, the running time can be reduced from exponential to polynomial by sampling. The analysis also shows that a gap of one on the value of [Formula: see text] for sampling can lead to an exponential difference on the expected running time, cautioning for a careful selection of [Formula: see text]. We further prove by using two illustrative examples that sampling can be more effective for noise handling than parent populations and threshold selection, two strategies that have shown to be robust to noise. Finally, we also show that sampling can be ineffective when noise does not bring a negative impact.

scholarly journals SATzilla: Portfolio-based Algorithm Selection for SAT

2008 ◽  
Vol 32 ◽  
pp. 565-606 ◽  
Author(s):  
L. Xu ◽  
F. Hutter ◽  
H. H. Hoos ◽  
K. Leyton-Brown
Keyword(s):  
Traditional Approach ◽  
Data Sets ◽  
Algorithm Selection ◽  
New Techniques ◽  
Algorithm Portfolios ◽  
Predicting Performance ◽  
Different Types ◽  
Sat Solver ◽  
Selection For ◽  
Problem Instances

It has been widely observed that there is no single "dominant" SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a per-instance basis. Building on previous work, we describe SATzilla, an automated approach for constructing per-instance algorithm portfolios for SAT that use so-called empirical hardness models to choose among their constituent solvers. This approach takes as input a distribution of problem instances and a set of component solvers, and constructs a portfolio optimizing a given objective function (such as mean runtime, percent of instances solved, or score in a competition). The excellent performance of SATzilla was independently verified in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one silver and one bronze medal. In this article, we go well beyond SATzilla07 by making the portfolio construction scalable and completely automated, and improving it by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances. We demonstrate the effectiveness of these new techniques in extensive experimental results on data sets including instances from the most recent SAT competition.

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