Evolution Under Strong Noise: A Self-Adaptive Evolution Strategy Can Reach the Lower Performance Bound - The pcCMSA-ES

Hellwig, Michael; Beyer, Hans-Georg

doi:10.1007/978-3-319-45823-6_3

Cited by 29 publications

(20 citation statements)

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“…Next, we implemented the pcCMSA algorithm [7,8] using a reference implementation provided by the authors 2 . Our implementation differs in two ways from [7,8]: Table 2: Learning-rates used by the CSA. All parameters are independent of t. For w i,t , we assume the indices of the points to be ordered by the ranks of function-values.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Another recent approach [7] is to estimate the slope of the progress in average function value and to use a hypothesis test to check whether the number of re-evaluations need to be increased. It has been shown that the full algorithm has O(1/T ) convergence rate [7,8]. The noise-handling approach of sampling additional points instead of re-evaluation has the advantage that it is compatible with other measures of function difficulty as presented in [14], where the number of sampled points is automatically increased on a function which is considered more difficult, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…multi-modal. However, the approach of [7] has the flaw that instead of weighted ranking, only selection of the better half of the points is performed. This ignores most of the information in the function values and leads to slow linear convergence rate in regions where the noise is small compared to the observed difference in function values.…”

Section: Introductionmentioning

confidence: 99%

“…This requires each point to be evaluated twice. In contrast to [7], our approach of measuring noise does not rely on additional assumptions on the shape of the function. This paper is structured as follows: The necessary background on ES is introduced in Section 2.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Large-scale noise-resilient evolution-strategies

Krause

2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Ranking-based Evolution Strategies (ES) are efficient algorithms for problems where gradient-information is not available or when the gradient is not informative. This makes ES interesting for Reinforcement-Learning (RL). However, in RL the high dimensionality of the search-space, as well as the noise of the simulations make direct adaptation of ES challenging. Noise makes ranking points difficult and a large budget of re-evaluations is needed to maintain a bounded error rate. In this work, the ranked weighting is replaced by a linear weighting function, which results in nearly unbiased stochastic gradient descent (SGD) on the manifold of probability distributions. The approach is theoretically analysed and the algorithm is adapted based on the results of the analysis. It is shown that in the limit of infinite dimensions, the algorithm becomes invariant to smooth monotonous transformations of the objective function. Further, drawing on the theory of SGD, an adaptation of the learning-rates based on the noise-level is proposed at the cost of a second evaluation for every sampled point. It is shown empirically that the proposed method improves on simple ES using Cumulative Step-size Adaptation and ranking. Further, it is shown that the proposed algorithm is more noise-resilient than a ranking-based approach.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Large-scale noise-resilient evolution-strategies

Krause

2019

Proceedings of the Genetic and Evolutionary Computation Conference

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TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz

Fent

et al. 2023

Journal of Field Robotics

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For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the IAC, which holds two competitions: A single‐vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from Technical University of Munich (TUM) placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around 2700.25em km h − 1 $270\,\text{km\hspace{0.05em}h}{}^{-1}$ and 280.25em m s − 2 $28\,ms{}^{-2}$. This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the 2‐year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. On the basis of this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real‐world evaluation of the displayed concepts.

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Population diversity control based differential evolution algorithm using fuzzy system for noisy multi-objective optimization problems

Subburaj,

Maheswari,

Ibrahim

et al. 2024

Sci Rep

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The objective measurements of the real-world optimization problems are mostly subject to noise which occurs due to several reasons like human measurement or environmental factors. The performance of the optimization algorithm gets affected if the effect of noise is higher than the negligible limit. The previous noise handling optimization algorithms use a large population size or multiple sampling at same region which increases the total count of function evaluations, and few methods work for a particular problem type. To address the above challenges, a Differential Evolution based Noise handling Optimization algorithm (NDE) to solve and optimize noisy bi-objective optimization problems is proposed. NDE is a Differential Evolution (DE) based optimization algorithm where the strategies for trial vector generation and the control parameters of DE algorithm are self-adapted using fuzzy inference system to improve the population diversity along the evolution process. In NDE, explicit averaging based method for denoising is used when the noise level is higher than negligible limit. Extending noise handling method enhances the performance of the optimization algorithm in solving real world optimization problems. To improve the convergence characteristics of the proposed algorithm, a restricted local search procedure is proposed. The performance of NDE algorithm is experimented using DTLZ and WFG problems, which are benchmark bi-objective optimization problems. The obtained results are compared with other SOTA algorithm using modified Inverted Generational Distance and Hypervolume performance metrics, from which it is confirmed that the proposed NDE algorithm is better in solving noisy bi-objective problems when compared to the other methods. To further strengthen the claim, statistical tests are conducted using the Wilcoxon and Friedman rank tests, and the proposed NDE algorithm shows significance over the other algorithms rejecting the null hypothesis.

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Evolution Under Strong Noise: A Self-Adaptive Evolution Strategy Can Reach the Lower Performance Bound - The pcCMSA-ES

Cited by 29 publications

References 10 publications

Large-scale noise-resilient evolution-strategies

Large-scale noise-resilient evolution-strategies

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Population diversity control based differential evolution algorithm using fuzzy system for noisy multi-objective optimization problems

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