Algorithm portfolios for noisy optimization

Cauwet, Marie-Liesse; Liu, Jialin; Rozière, Baptiste; Teytaud, Olivier

doi:10.1007/s10472-015-9486-2

Cited by 16 publications

(6 citation statements)

References 40 publications

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“…Sophisticated approaches have been proposed to address the noisy optimization issue (see e.g. [3]). Another approach is proposed here, based on the bootstrap principle: in each CMA-ES generation, the set of n problem instances used to compute the performance is uniformly drawn with replacement from the n-size training set.…”

Section: Asapv2mentioning

confidence: 99%

Algorithm Selector and Prescheduler in the ICON Challenge

Gonard

Schoenauer

Sebag

2018

Bioinspired Heuristics for Optimization

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Acknowledgements This work has been carried out in the framework of IRT SystemX and therefore granted with public funds within the scope of the French Program "Investissements d'Avenir".Abstract Algorithm portfolios are known to offer robust performances, efficiently overcoming the weakness of every single algorithm on some particular problem instances. Two complementary approaches to get the best out of an algorithm portfolio is to achieve algorithm selection (AS), and to define a scheduler, sequentially launching a few algorithms on a limited computational budget each. The presented Algorithm Selector And Prescheduler system relies on the joint optimization of a pre-scheduler and a per instance AS, selecting an algorithm well-suited to the problem instance at hand. ASAP has been thoroughly evaluated against the state-ofthe-art during the ICON challenge for algorithm selection, receiving an honourable mention. Its evaluation on several combinatorial optimization benchmarks exposes surprisingly good results of the simple heuristics used; some extensions thereof are presented and discussed in the paper.

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Section: Asapv2mentioning

confidence: 99%

Algorithm Selector and Prescheduler in the ICON Challenge

Gonard

Schoenauer

Sebag

2018

Bioinspired Heuristics for Optimization

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“…In order to address this challenge, algorithm portfolio is employed to reduce the risk of failing to optimise problems in multiple scenarios [9]. For example, the algorithm portfolio obtained an excellent performance in SAT problems [25] and noisy optimization problem [4,5]. Although many portfolio strategies have been proposed for noise free evolutionary algorithms [3,16,21,27] and Bayesian optimisation [8], to the best of our knowledge, we are the first to apply the algorithm portfolio for individual-based SAEAs which has shown outstanding performance in solving CEPs.…”

Section: Introductionmentioning

confidence: 99%

Algorithm portfolio for individual-based surrogate-assisted evolutionary algorithms

Hao

Liu

Yao

2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in solving unknown problems due to no free lunch theorems, and it will cause more computational resource if we re-run the algorithm or try other algorithms to get a much solution, which is more serious in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce the risk of choosing an inappropriate algorithm for CEPs. We propose two portfolio frameworks for very expensive problems in which the maximal number of fitness evaluations is only 5 times of the problem's dimension. One framework named Par-IBSAEA runs all algorithm candidates in parallel and a more sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound (UCB) policy from reinforcement learning to help select the most appropriate algorithm at each iteration. An effective reward definition is proposed for the UCB policy. We consider three state-of-the-art individual-based SAEAs on different problems and compare them to the portfolios built from their instances on several benchmark problems given limited computation budgets. Our experimental studies demonstrate that our proposed portfolio frameworks significantly outperform any single algorithm on the set of benchmark problems.

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“…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

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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).

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Algorithm portfolios for noisy optimization

Cited by 16 publications

References 40 publications

Algorithm Selector and Prescheduler in the ICON Challenge

Algorithm Selector and Prescheduler in the ICON Challenge

Algorithm portfolio for individual-based surrogate-assisted evolutionary algorithms

Sorting by Swaps with Noisy Comparisons

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