Autonomous operator management for evolutionary algorithms

Maturana, Jorge; Lardeux, Frédéric; Saubion, Frédéric

doi:10.1007/s10732-010-9125-3

Cited by 75 publications

(57 citation statements)

References 36 publications

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“…order to efficiently tackle multi-modal problems, the diversity should also be considered somehow by the Credit Assignment, following the ideas of [31,32].…”

Section: Discussionmentioning

confidence: 99%

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Analyzing bandit-based adaptive operator selection mechanisms

Fialho

Costa

Schoenauer

et al. 2010

Ann Math Artif Intell

135

123

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Several techniques have been proposed to tackle the Adaptive Operator Selection (AOS) issue in Evolutionary Algorithms. Some recent proposals are based on the Multi-Armed Bandit (MAB) paradigm: each operator is viewed as one arm of a MAB problem, and the rewards are mainly based on the fitness improvement brought by the corresponding operator to the individual it is applied to. However, the AOS problem is dynamic, whereas standard MAB algorithms are known to optimally solve the exploitation versus exploration trade-off in static settings. An original dynamic variant of the standard MAB Upper Confidence Bound algorithm is proposed here, using a sliding time window to compute both its exploitation and exploration terms. In order to perform sound comparisons between AOS algorithms, artificial scenarios have been proposed in the literature. They are extended here toward smoother transitions between different reward settings. The resulting original testbed also includes a real evolutionary algorithm that is applied to the well-known Royal Road problem. It is used here to perform a thorough analysis of the behavior of AOS algorithms, to assess their sensitivity with respect to their own hyper-parameters, and to propose a sound comparison of their performances.

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“…order to efficiently tackle multi-modal problems, the diversity should also be considered somehow by the Credit Assignment, following the ideas of [31,32].…”

Section: Discussionmentioning

confidence: 99%

“…Along this line, [33] proposed another credit measure called Compass, defined as a weighted sum of fitness improvement (intensification) and offspring diversity (diversification). In [32], a different aggregation between both impact measures was proposed, based on the Pareto Dominance paradigm.…”

Section: Credit Assignmentmentioning

confidence: 99%

Analyzing bandit-based adaptive operator selection mechanisms

Fialho

Costa

Schoenauer

et al. 2010

Ann Math Artif Intell

135

123

View full text Add to dashboard Cite

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“…This evaluation criterion does not regard the fitness as the unique criterion, as the authors argue that infrequent but powerful operators are as significant as frequent but powerless operators. Density estimator is adopted as evaluation criterion in [41,42], and a statistic method is added in [42]. This method calculates the normalized relative fitness improvements from successful operators, and then it regards the mean value of the improvements brought by operators as the credit.…”

Section: Credit Assignmentmentioning

confidence: 99%

Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

Gao

Qing-hua

Fan

et al. 2017

Mathematical Problems in Engineering

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The adaptive operator selection (AOS) and the adaptive parameter control are widely used to enhance the search power in many multiobjective evolutionary algorithms. This paper proposes a novel adaptive selection strategy with bandits for the multiobjective evolutionary algorithm based on decomposition (MOEA/D), named latest stored information based adaptive selection (LSIAS). An improved upper confidence bound (UCB) method is adopted in the strategy, in which the operator usage rate and abandonment of extreme fitness improvement are introduced to improve the performance of UCB. The strategy uses a sliding window to store recent valuable information about operators, such as factors, probabilities, and efficiency. Four common used DE operators are chosen with the AOS, and two kinds of assist information on operator are selected to improve the operators search power. The operator information is updated with the help of LSIAS and the resulting algorithmic combination is called MOEA/D-LSIAS. Compared to some well-known MOEA/D variants, the LSIAS demonstrates the superior robustness and fast convergence for various multiobjective optimization problems. The comparative experiments also demonstrate improved search power of operators with different assist information on different problems.

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“…However, there are methodologies that can cut across categories. For example, we can see hybrid methodologies that combine constructive with perturbation heuristics [51], or heuristic selection with heuristic generation [42,60,66,71].…”

Section: Hyper-heuristicsmentioning

confidence: 99%

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

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The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to 123Genet Program Evolvable Mach DOI 10.1007/s10710-013-9186-9 promote awareness and cross-fertilisation of ideas across the (by and large, nonoverlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.

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Autonomous operator management for evolutionary algorithms

Cited by 75 publications

References 36 publications

Analyzing bandit-based adaptive operator selection mechanisms

Analyzing bandit-based adaptive operator selection mechanisms

Latest Stored Information Based Adaptive Selection Strategy for Multiobjective Evolutionary Algorithm

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

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