15.01.2020

Two papers accepted at ECAI and EvoCOP

Europe’s premier AI Research venue, the European Conference on Artificial Intelligence (ECAI), holds its 24^th edition in June in Santiago de Compostela, Spain. The Algorithm Engineering group contributes one paper. This work suggests that a simple greedy algorithm can be used to maximize non-monotone submodular functions under complex knapsack constraints. This problem finds several applications in AI and machine learning.

In addition to that, the 20th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP) accepted another paper of the group. This paper disproves a previous claim that a certain type of evolutionary algorithm is not well suited for optimizing a deceptive landscape. In fact, the considered algorithm is even more efficient than any competing algorithm deemed efficient for that setting. Due to this surprising insight, the paper is nominated for a best-paper award.

Clean Citation Style

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Doskoč, Vanja; Friedrich, Tobias; Göbel, Andreas; Neumann, Aneta; Neumann, Frank; Quinzan, FrancescoNon-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings. European Conference on Artificial Intelligence (ECAI) 2020: 435-442
[ Abstract ] [ BibTeX ] [ DOI ] [ Download ]

We study the problem of maximizing a non-monotone submodular function under multiple knapsack constraints. We propose a simple discrete greedy algorithm to approach this problem, and prove that it yields strong approximation guarantees for functions with bounded curvature. In contrast to other heuristics, this does not require problem relaxation to continuous domains and it maintains a constant-factor approximation guarantee in the problem size. In the case of a single knapsack, our analysis suggests that the standard greedy can be used in non-monotone settings. Additionally, we study this problem in a dynamic setting, in which knapsacks change during the optimization process. We modify our greedy algorithm to avoid a complete restart at each constraint update. This modification retains the approximation guarantees of the static case. We evaluate our results experimentally on a video summarization and sensor placement task. We show that our proposed algorithm competes with the state-of-the-art in static settings. Furthermore, we show that in dynamic settings with tight computational time budget, our modified greedy yields significant improvements over starting the greedy from scratch, in terms of the solution quality achieved.
@inproceedings{doskoc2020nonmonotone, abstract = {We study the problem of maximizing a non-monotone submodular function under multiple knapsack constraints. We propose a simple discrete greedy algorithm to approach this problem, and prove that it yields strong approximation guarantees for functions with bounded curvature. In contrast to other heuristics, this does not require problem relaxation to continuous domains and it maintains a constant-factor approximation guarantee in the problem size. In the case of a single knapsack, our analysis suggests that the standard greedy can be used in non-monotone settings. Additionally, we study this problem in a dynamic setting, in which knapsacks change during the optimization process. We modify our greedy algorithm to avoid a complete restart at each constraint update. This modification retains the approximation guarantees of the static case. We evaluate our results experimentally on a video summarization and sensor placement task. We show that our proposed algorithm competes with the state-of-the-art in static settings. Furthermore, we show that in dynamic settings with tight computational time budget, our modified greedy yields significant improvements over starting the greedy from scratch, in terms of the solution quality achieved.}, author = {Doskoč, Vanja and Friedrich, Tobias and Göbel, Andreas and Neumann, Aneta and Neumann, Frank and Quinzan, Francesco}, booktitle = {European Conference on Artificial Intelligence (ECAI)}, keywords = {anetaneumann vanjadoskoc andreasgoebel francescoquinzan tobiasfriedrich ecai frankneumann year2020}, pages = {435-442}, publisher = {{IOS} Press}, title = {Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings}, year = 2020 }
Doerr, Benjamin; Krejca, Martin S.The Univariate Marginal Distribution Algorithm Copes Well With Deception and Epistasis. Evolutionary Computation in Combinatorial Optimisation (EvoCOP) 2020: 51-66
[ Abstract ] [ BibTeX ] [ DOI ] [ Download ]

In their recent work, Lehre and Nguyen (FOGA 2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceivingLeadingBlocks (DLB) problem. They conclude from this result that univariate EDAs have difficulties with deception and epistasis. In this work, we show that this negative finding is caused by an unfortunate choice of the parameters of the UMDA. When the population sizes are chosen large enough to prevent genetic drift, then the UMDA optimizes the DLB problem with high probability with at most \(\lambda(\frac{n}{2} + 2 e \ln n)\) fitness evaluations. Since an offspring population size \(\lambda\) of order \(n \log n\) can prevent genetic drift, the UMDA can solve the DLB problem with \(O(n^2 \log n)\) fitness evaluations. In contrast, for classic evolutionary algorithms no better run time guarantee than \(O(n^3)\) is known, so our result rather suggests that the UMDA can cope well with deception and epistatis. Together with the result of Lehre and Nguyen, our result for the first time rigorously proves that running EDAs in the regime with genetic drift can lead to drastic performance losses.
@inproceedings{doerr2020univariate, abstract = {In their recent work, Lehre and Nguyen (FOGA 2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceivingLeadingBlocks (DLB) problem. They conclude from this result that univariate EDAs have difficulties with deception and epistasis. In this work, we show that this negative finding is caused by an unfortunate choice of the parameters of the UMDA. When the population sizes are chosen large enough to prevent genetic drift, then the UMDA optimizes the DLB problem with high probability with at most \(\lambda(\frac{n}{2} + 2 e \ln n)\) fitness evaluations. Since an offspring population size \(\lambda\) of order \(n \log n\) can prevent genetic drift, the UMDA can solve the DLB problem with \(O(n^2 \log n)\) fitness evaluations. In contrast, for classic evolutionary algorithms no better run time guarantee than \(O(n^3)\) is known, so our result rather suggests that the UMDA can cope well with deception and epistatis. Together with the result of Lehre and Nguyen, our result for the first time rigorously proves that running EDAs in the regime with genetic drift can lead to drastic performance losses.}, author = {Doerr, Benjamin and Krejca, Martin S.}, booktitle = {Evolutionary Computation in Combinatorial Optimisation (EvoCOP)}, keywords = {martinskrejca evocop benjamindoerr year2020}, note = {Best-Paper Award}, pages = {51-66}, publisher = {Springer}, title = {The Univariate Marginal Distribution Algorithm Copes Well With Deception and Epistasis}, year = 2020 }