Adaptive Operator Selection and Management in Evolutionary Algorithms

Maturana, Jorge; Fialho, Álvaro; Saubion, Frédéric; Schoenauer, Marc; Lardeux, Frédéric; Sebag, Michèle

doi:10.1007/978-3-642-21434-9_7

Cited by 8 publications

(13 citation statements)

References 32 publications

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“…However, as mentioned above, existing works almost exclusively focus on changing parameters of selected components of an otherwise stable algorithmic framework. is includes most works on hyper-heuristics [6] and related concepts such as adaptive operator selection [24], and parameter control [10].…”

Section: Related Workmentioning

confidence: 99%

“…State-of-the-art optimization algorithms therefore o en incorporate mechanisms to adjust their search behavior while optimizing, by taking into account the information obtained during the run. ese techniques are studied under many di erent umbrellas, such as parameter control [10], meta-heuristics [5], adaptive operator selection [24], or hyper-heuristics [6]. e probably best-known and most widely used techniques for achieving a dynamic search behavior are the one-h success rule [7,32,33] and the covariance adaptation technique that the family of CMA-ES algorithms [14,15] GECCO '20, Cancn, Mexico 2020.…”

Section: Introductionmentioning

confidence: 99%

“…We perform a rst assessment of the performance that one could expect to see when applying dynAS to the algorithms in the BBOB data sets, to understand whether the gains would justify further exploration of the dynAS paradigm on this test-bed. We nd that -even when restricting the dynAS problem further to allowing only a single switch between algorithms in the portfolio -promising improvements over the best static solvers can be expected, in particular for the more complex problems (functions [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards dynamic algorithm selection for numerical black-box optimization

Vermetten¹,

Wang

Bäck³

et al. 2020

Proceedings of the 2020 Genetic and Evolutionary Computation Conference

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One of the most challenging problems in evolutionary computation is to select from its family of diverse solvers one that performs well on a given problem. is algorithm selection problem is complicated by the fact that di erent phases of the optimization process require di erent search behavior. While this can partly be controlled by the algorithm itself, there exist large di erences between algorithm performance. It can therefore be bene cial to swap the con guration or even the entire algorithm during the run. Long deemed impractical, recent advances in Machine Learning and in exploratory landscape analysis give hope that this dynamic algorithm con guration (dynAC) can eventually be solved by automatically trained con guration schedules. With this work we aim at promoting research on dynAC, by introducing a simpler variant that focuses only on switching between di erent algorithms, not congurations. Using the rich data from the Black Box Optimization Benchmark (BBOB) platform, we show that even single-switch dynamic Algorithm selection (dynAS) can potentially result in signicant performance gains. We also discuss key challenges in dynAS, and argue that the BBOB-framework can become a useful tool in overcoming these. CCS CONCEPTS• eory of computation → Bio-inspired optimization; Online algorithms;arXiv:2006.06586v1 [cs.NE]

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards dynamic algorithm selection for numerical black-box optimization

Vermetten¹,

Wang

Bäck³

et al. 2020

Proceedings of the 2020 Genetic and Evolutionary Computation Conference

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“…Given a permutation π, the longest increasing subsequence LIS(π) corresponds to the longest subsequence of elements of π that are sorted in ascending order. For instance, for π = (1, 3, 2, 4) the longest increasing subsequences are (1, 2, 4) and (1,3,4).…”

Section: Product Of Permutationsmentioning

confidence: 99%

“…Therefore, given a new permutation problem a non-expert user would hardly be able to design a solving algorithm using components that she/he could re-use from previous experiments. A recent trend in optimization consists in promoting more autonomous techniques for the design of search algorithms [1], either by automating the tuning of their parameters [2], by dynamically controlling their behaviour [3] or by automating their design [4]. Focusing on this latest aspect, hyperheuristics [5] is a generic paradigm that can be used to manage a set of efficient heuristics in order to solve a sufficiently large set of problem instances, without a priori knowledge.…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Operators for Permutation Based Problems

Desport

Basseur²,

Goëffon³

et al. 2015

Lecture Notes in Computer Science

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Abstract. This paper presents an analysis of different possible operators for local search algorithms in order to solve permutation-based problems. These operators can be defined by a distance metric that define the neighborhood of the current configuration, and a selector that chooses the next configuration to be explored within this neighborhood. The performance of local search algorithms strongly depends on their ability to efficiently explore and exploit the search space. We propose here a methodological approach in order to study the properties of distances and selectors in order to buildtheir performances operators that can be used either for intensification of the search or for diversification stages. Based on different observations, this approach allows us to define a simple generic hyperheuristic that adapt the choice of its operators to the problem at hand and that manages their use in order to ensure a good trade-off between intensification and diversification. Moreover this hyperheuristic can be used on different permutation-based problems.

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On branching heuristics for the bi-objective 0/1 unidimensional knapsack problem

et al. 2017

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International audienceThis paper focuses on branching strategies that are involved in branch and bound algorithms when solving multi-objective optimization problems. The choice of the branching variable at each node of the search tree constitutes indeed an important component of these algorithms. In this work we focus on multi-objective knapsack problems. In the literature, branching heuristics used for these problems are static, i.e., the order on the variables is determined prior to the execution. This study investigates the benefit of defining more sophisticated branching strategies. We first analyze and compare a representative set of classic branching heuristics and conclude that none can be identified as the best overall heuristic. Using an oracle, we highlight that combining branching heuristics within the same branch and bound algorithm leads to considerably reduced search trees but induces high computational costs. Based on learning adaptive techniques, we propose then dynamic adaptive branching strategies that are able to select the suitable heuristic to apply at each node of the search tree. Experiments are conducted on the bi-objective 0/1 unidimensional knapsack problem

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Adaptive Operator Selection and Management in Evolutionary Algorithms

Cited by 8 publications

References 32 publications

Towards dynamic algorithm selection for numerical black-box optimization

Towards dynamic algorithm selection for numerical black-box optimization

Empirical Analysis of Operators for Permutation Based Problems

On branching heuristics for the bi-objective 0/1 unidimensional knapsack problem

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