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Stephen Friess, "Inductive Biases and Metaknowledge Representations for Search-based Optimization", University of Birmingham, 2022.

Abstract

"What I do not understand, I can still create.", H. Sayama. The following work follows closely the aforementioned bonmot. Guided by questions such as: "How can evolutionary processes exhibit learning behavior and consolidate knowledge?", "What are cognitive models of problem-solving?" and "How can we harness these altogether as computational techniques?", we clarify within this work essentials required to implement them for metaheuristic search a...



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Xilu Wang, "Bayesian Evolutionary Optimization for Heterogeneously Expensive Multi-objective Optimization", University of Surrey, 2022.

Abstract

Various multi-objective optimization algorithms have been proposed with a common assumption that the evaluation of each objective function takes the same period of time. Little attention has been paid to more general and realistic optimization scenarios where different objectives are evaluated by different computer simulations or physical experiments with different time complexities (latencies) and only a very limited number of function evalua...



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Hao Tong, Leandro Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao, "What Makes The Dynamic Capacitated Arc Routing Problem Hard To Solve: Insights From Fitness Landscape Analysis", The Genetic and Evolutionary Computation Conference, 2022.

Abstract

The Capacitated Arc Routing Problem (CARP) aims at assigning vehicles to serve tasks which are located at different arcs in a graph. However, the originally planned routes are easily affected by different dynamic events like newly added tasks. This gives rise to Dynamic CARP (DCARP) instances, which need to be efficiently optimized for new high-quality service plans in a short time. However, it is unknown which dynamic events make DCARP instances...



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Jan Goepfert, Heiko Wersing, Barbara Hammer, Lukas Hindemith, "Intuitiveness in Active Teaching", IEEE Transactions on Human-Machine Systems, vol. 52, no. 3, pp. 458 - 467, 2022.

Abstract

Machine learning is a double-edged sword: it gives rise to astonishing results in automated systems, but at the cost of tremendously large data requirements. This makes many successful algorithms from machine learn- ing unsuitable for human-machine interaction, where the machine must learn from a small number of training samples that can be provided by a user within a reasonable time frame. Fortunately, the user can tailor the training data...



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Ernest Hutapea, Nivesh Dommaraju, Mariusz Bujny, Fabian Duddeck, "Clustering Topologically-Optimized Designs based on Structural Deformation", Munich Symposium on Lightweight Design 2021, 2022.

Abstract

Topology optimization can be used to generate a large set of lightweight structural solutions either by changing the constraints or the weights for different objectives in multi-objective optimization. Engineers must analyze and review the designs to select solutions according to their preference towards objectives such as structural compliance and crash performance. However, the sheer number of solutions challenge the engineers' decision-making ...



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Duc Anh Nguyen, Anna Kononova, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck, "An Efficient Contesting Procedure for AutoML Optimization", IEEE Access, 2022.

Abstract

Automated Machine Learning (AutoML) frameworks are designed to select the optimal combination of operators and hyperparameters. Classical AutoML-based Bayesian Optimization (BO) approaches often integrate all operator search spaces into a single search space. However, a disadvantage of this history-based strategy is that it can be less robust when initialized randomly than optimizing each operator algorithm combination independently. To overcome ...



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Stephen Friess, Peter Tino, Stefan Menzel, Zhao Xu, Bernhard Sendhoff, Xin Yao, "Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction", International Joint Conference on Neural Networks, 2022.

Abstract

Traditional methods for solving problems within computer science rely mostly upon the application of handcrafted algorithms. As however manual engineering of them can be considered to be a tedious process, it is interesting to consider how far internal mechanisms can be directly learned in an end-to-end manner instead. This is especially tempting when considering metaheuristic and evolutionary optimization routines which rely inherently upon stoc...



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Hao Tong, Leandro Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao, "Benchmarking Dynamic Capacitated Arc Routing Algorithms Using Real-World Traffic Simulation", IEEE Congress on Evolutionary Computation, 2022.

Abstract

The combinatorial optimization of the dynamic capacitated arc routing problem (DCARP) targets to re-schedule the service plans of agents, such as vehicles in a city scenario, when dynamic events deteriorate the quality of the current schedule. Various algorithms have been proposed to solve DCARP instances in different dynamic scenarios. However, most existing work in literature developed algorithms and evaluated their performance based on artifi ...



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Jonathan Jakob, Martina Hasenjäger, Barbara Hammer, "Reject Options for Incremental Regression Scenarios", International Conference on Artificial Neural Networks (ICANN) 2022, 2022.

Abstract

Machine Learning with a Reject Option is the empowerment of an algorithm to abstain from prediction when the outcome is likely to be inaccurate. Although, already studied many decades ago, this field of machine learning has recently gained some traction again. However, most reject option applications concern themselves with classification tasks and from the little work that is available for regression systems all are about rejections in an offlin...



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Jonathan Jakob, André Artelt, Martina Hasenjäger, Barbara Hammer, "SAMknn Regressor for Online Learning in Water Distribution Networks", International Conference on Artificial Neural Networks (ICANN) 2022, 2022.

Abstract

Water distribution networks are a key component of modern infrastructure for housing and industry. They transport and distribute water via widely branched networks from sources to the houses, buildings and industrial plants where it is consumed. In the flow of these networks anomalies can manifest themselves e.g. through leakages and or other unforeseen behaviour like fire runs. Since, each anomaly has the potential of being a leakage problem whe...



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