A mathematically derived number of resamplings for noisy optimization

Liu, Jialin; St-Pierre, David Lupien; Teytaud, Olivier

doi:10.1145/2598394.2598458

Cited by 12 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each replication of a UCARP is a DCARP instance as the random variables are replaced by their deterministic realizations, usually via Monte Carlo simulations. This technique is also called "resampling" in noisy optimization aiming at reducing the probability of mis-ranking solutions [48], [49]. This framework assumes that the model of the random variables, or at least, the expectation of the random variables, perfectly reflects the true one in real life.…”

Section: ) Deterministic Optimization Stochastic Evaluation-solvingmentioning

confidence: 99%

Robust Optimization in Uncertain Capacitated Arc Routing Problems: Progresses and Perspectives [Review Article]

Liu

Tang

Yao

2021

IEEE Comput. Intell. Mag.

Self Cite

View full text Add to dashboard Cite

T he capacitated arc routing problem is an important NP-hard problem with numerous realworld applications. The capacitated arc routing problem with uncertainties refers to those instances where there are uncertainties in decision variables, objective functions and/or constraints. The capacitated arc routing problem with uncertainties captures real-world situations much better than a static capacitated arc routing problem because few real-world problems are static and certain. Uncertainties in the capacitated arc routing problem pose new research challenges. Algorithms that work well for a static and certain capacitated arc routing problem may not work on the version with uncertainties. There have been increasing progresses in studying the capacitated arc routing problem with uncertainties during the past two decades. However, the papers on the capacitated arc routing problem with uncertainties have been scattered around in different journals and conferences in artificial intelligence, computer science, and operational research. Different definitions and formulations of capacitated arc routing problem with uncertainties are used by different papers, making comparisons difficult. In order to better understand the state-of-the-art in solving the capacitated arc routing problem with uncertainties, this paper presents a comprehensive review of the problem and its key research issues. Not only has the paper summarized the progresses so far, key research issues are identified, including scalability of the algorithms, perfor mance measures, common benchmarks, etc. Future research directions are also identified at the end of this review.

show abstract

Section: ) Deterministic Optimization Stochastic Evaluation-solvingmentioning

confidence: 99%

Robust Optimization in Uncertain Capacitated Arc Routing Problems: Progresses and Perspectives [Review Article]

Liu

Tang

Yao

2021

IEEE Comput. Intell. Mag.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Averaging decreases the variance of fitness but the problem is that it is not clear in advance what would be the sample size used for averaging (Aizawa and Wah, 1994). Most authors use several measures of fitness for each new individual (Costa et al, 2013), although other averaging strategies have also been proposed, like averaging over the neighbourhood of the individual or using resampling, that is, more measures of fitness in a number which is decided heuristically (Liu et al, 2014). This assumes that there is, effectively, an average of the fitness values which is true for Gaussian random noise and other distributions such as Gamma or Cauchy but not necessarily for all distributions.…”

Section: State Of the Artmentioning

confidence: 99%

Studying and Tackling Noisy Fitness in Evolutionary Design of Game Characters

Merelo

Castillo

Mora

et al. 2014

Proceedings of the International Conference on Evolutionary Computation Theory and Applications

View full text Add to dashboard Cite

In most computer games as in life, the outcome of a match is uncertain due to several reasons: the characters or assets appear in different initial positions or the response of the player, even if programmed, is not deterministic; different matches will yield different scores. That is a problem when optimizing a game-playing engine: its fitness will be noisy, and if we use an evolutionary algorithm it will have to deal with it. This is not straightforward since there is an inherent uncertainty in the true value of the fitness of an individual, or rather whether one chromosome is better than another, thus making it preferable for selection. Several methods based on implicit or explicit average or changes in the selection of individuals for the next generation have been proposed in the past, but they involve a substantial redesign of the algorithm and the software used to solve the problem. In this paper we propose new methods based on incremental computation (memory-based) or fitness average or, additionally, using statistical tests to impose a partial order on the population; this partial order is considered to assign a fitness value to every individual which can be used straightforwardly in any selection function. Tests using several hard combinatorial optimization problems show that, despite an increased computation time with respect to the other methods, both memory-based methods have a higher success rate than implicit averaging methods that do not use memory; however, there is not a clear advantage in success rate or algorithmic terms of one method over the other.

show abstract

“…An important and classical aspect of EAs is how robust their performance is in the presence of noise [15,8]. This theme has gained increased attention in the last few years, [16,29,9,3,5,4,1,27,34,33,20], see [35] for a comprehensive review. Mostly, noise is modeled by imperfect fitness function evaluations thatinstead of the exact fitness value -return a perturbed value (e.g., by a Gaussian additive term).…”

Section: Introductionmentioning

confidence: 99%

Sorting by Swaps with Noisy Comparisons

2018

View full text Add to dashboard Cite

We study sorting of permutations by random swaps if each comparison gives the wrong result with some fixed probability p < 1/2. We use this process as prototype for the behaviour of randomized, comparisonbased optimization heuristics in the presence of noisy comparisons. As quality measure, we compute the expected fitness of the stationary distribution. To measure the runtime, we compute the minimal number of steps after which the average fitness approximates the expected fitness of the stationary distribution.We study the process where in each round a random pair of elements at distance at most r are compared. We give theoretical results for the extreme cases r = 1 and r = n, and experimental results for the intermediate cases. We find a trade-off between faster convergence (for large r) and better quality of the solution after convergence (for small r).

show abstract

A mathematically derived number of resamplings for noisy optimization

Cited by 12 publications

References 9 publications

Robust Optimization in Uncertain Capacitated Arc Routing Problems: Progresses and Perspectives [Review Article]

Robust Optimization in Uncertain Capacitated Arc Routing Problems: Progresses and Perspectives [Review Article]

Studying and Tackling Noisy Fitness in Evolutionary Design of Game Characters

Sorting by Swaps with Noisy Comparisons

Contact Info

Product

Resources

About