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Using the Pilot Method as a Problem-Independent Metaheuristic for Multi-objective Beam Search

Using the Pilot Method as a Problem-Independent Metaheuri...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Bindreiter, Oliver Werth, Bernhard Wagner, Stefan Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg4232 Austria

ISBN: (纸本)9783031838873

This paper proposes a combination of multi-objective beam search and the pilot method. The idea is to use the pilot method as a problem-independent metaheuristic to guide the search process of multi-objective beam search. Through this combination, the newly created search algorithm is less dependent on problem specific heuristics, which would otherwise be required to guide the search process. Especially when dealing with problems where no well-known heuristic functions exist this approach can be feasible. It must be noted, that in this situation, the design of the required heuristic functions is a challenging task because a deep understanding of the underlying problem as well as the chosen search algorithm is required. The proposed methodology does not use multiple heuristic functions to evaluate nodes. Instead, a sub search process in the form of the pilot method is started to obtain an estimation for the node’s quality. This sub search process is a single target search process and can only provide an estimation for one of the problems target functions. Therefore, the target functions are now also used as heuristic functions. Applying this new search algorithm to well-known benchmark problems yielded promising results and generates high quality solutions compared with current state-of-the-art algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multiobjective optimization

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Age-Layer-Population-Structure with Self-adaptation in Optimization

Age-Layer-Population-Structure with Self-adaptation in Opt...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Yang, Kaifeng Werth, Bernhard Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg4232 Austria

ISBN: (纸本)9783031838873

Dynamic optimization problems are a class of optimization problems where the objective function, constraints, or both can change over time. In this work, we address the dynamic travelling salesman problem (TSP) by using the age-layered population structure (ALPS). To enhance different layer’s behaviour, we introduce the self-adaptation strategies to adjust the mutation and crossover rate in each layers, which are stationary in the conventional ALPS. The proposed strategies are compared with the stationary strategy over 7 different dynamic TSPs. The experimental results shows that the proposed strategy, convex strategy, yields much better results than the stationary strategy on complex problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： evolutionary algorithms

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A Comparison of Recent algorithms for Symbolic Regression to Genetic Programming

A Comparison of Recent Algorithms for Symbolic Regression ...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Radwan, Yousef A. Kronberger, Gabriel Winkler, Stephan University of Applied Sciences Upper Austria Heuristic and Evolutionary Algorithms Laboratory Softwarepark 11 Hagenberg4232 Austria

ISBN: (纸本)9783031829512

Symbolic regression is a machine learning method with the goal to produce interpretable results. Unlike other machine learning methods such as, e.g. random forests or neural networks, which are opaque, symbolic regression aims to model and map data in a way that can be understood by scientists. Recent advancements, have attempted to bridge the gap between these two fields;new methodologies attempt to fuse the mapping power of neural networks and deep learning techniques with the explanatory power of symbolic regression. In this paper, we examine these new emerging systems and test the performance of an end-to-end transformer model for symbolic regression versus the reigning traditional methods based on genetic programming that have spearheaded symbolic regression throughout the years. We compare these systems on novel datasets to avoid bias to older methods who were improved on well-known benchmark datasets. Our results show that traditional GP methods as implemented e.g., by Operon still remain superior to two recently published symbolic regression methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Domain Knowledge

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Incrementally Solving the Dynamic Stacking Problem

Incrementally Solving the Dynamic Stacking Problem

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Leitner, Sebastian Wagner, Stefan Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg4232 Austria Josef Ressel Center for Adaptive Optimization in Dynamic Environments Hagenberg Austria

ISBN: (纸本)9783031838873

In this paper we tackle the dynamic stacking problem by introducing a framework for incremental online optimization. The dynamic stacking problem features continuous uncertain arrival and delivery of blocks via a crane controlled by the solver. The problem is implemented as a discrete event simulation and the solver runs asynchronously. We develop a framework that can use our existing offline solver for the dynamic stacking problem and turn it into an online solver capable of incrementally updating optimized plans. We test our framework by comparing to our previously published iterative approach as well as a rule based baseline solver on a diverse set of problem instances. Using the new framework, the solver improves our key performance indicators across the benchmark instances. We also investigate the reasons for the performance differences both in the aggregate as well as the level of individual simulation runs. The framework not only works well on this specific stacking problem, but is general enough to be used in many online dynamic optimization problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Discrete event simulation

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Comparing Constraint Evaluation Methods for Shape-Constrained Regression

Comparing Constraint Evaluation Methods for Shape-Constrain...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Haider, Christian Bachinger, Florian Holzinger, Florian de França, Fabrício Olivetti Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Hagenberg Austria Johannes Kepler University Linz Austria Center for Mathematics Computing and Cognition Federal University of ABC Santo André Brazil

ISBN: (纸本)9783031829512

Shape-constrained regression is an important desideratum of data-based modeling when you want to enforce your model to possess an expected behavior despite the intrinsic noise of the collected data. Conventional data-based modeling approaches are solely reliant on empirical data, and thus, often fail to incorporate the essential physical constraints inherent to the underlying systems. This leads to a lack of trust in the model and a reduction of explanatory power. Recognizing these limitations of traditional data-based methodologies, the integration of shape constraints, derived from domain expertise or fundamental physical principles, emerges as a promising extension to enhance the quality of models. In this paper, we distinguish between single-objective and multi-objective approaches, as well as soft and hard constraint methods to evaluate the constraint violations of models. In the context of this research, the primary focus is on single-objective approaches that incorporate an expanded scope of features and dynamics for constraint handling. Instead of strictly enforcing hard constraints and discarding solutions solely based on constraint violation, the paper presents a dynamic method for calculating constraint violation and prediction error. The presented approach involves an incremental increase in the weighting of constraint violation during the modeling process, achieving an initial, wide-ranging exploration of the solution space followed by a comprehensive investigation of potential models through an in-depth search. Furthermore, we want to explore the critical issue of premature stagnation, a phenomenon wherein the search for a conformant model, plateaus before reaching an acceptable local optima solution. This paper tries to address this challenge through different strategies to maintain a certain diversity of solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Regression analysis

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Machine Learning Update Strategies for Real-Time Production Environments

Machine Learning Update Strategies for Real-Time Production...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Neuhauser, Philipp Fleck, Philipp Leitner, Sebastian Wagner, Stefan Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Wels Austria Josef Ressel Center for Adaptive Optimization in Dynamic Environments University of Applied Sciences Upper Austria Wels Austria

ISBN: (纸本)9783031838873

Modern application scenarios in the dynamic industrial, financial, and economic sectors increasingly require quick and agile machine learning solutions. Instead of waiting hours for batch processing systems to deliver results, these systems should ideally adapt and make decisions as soon as new data comes in. As the demand for real-time machine learning solutions using streaming data is steadily increasing, this paper explores a software architecture that efficiently combines the Apache Kafka ecosystem with Microsoft’s machine learning framework *** for reliable data processing and model adaptation. The research addresses the complexity of deciding when to re-train these models in an unbounded data stream context. Various update strategies, including periodic, and performance-based model training, are evaluated for effectiveness under different conditions. The goal of this paper is to propose a completely autonomous machine learning pipeline that is capable of keeping models updated while minimizing computational costs required for re-training and ensuring prediction accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data streams

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Re-evaluation in Dynamic Tree-Search with Backtracking from Known Solutions

Re-evaluation in Dynamic Tree-Search with Backtracking fro...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Fleck, Philipp Neuhauser, Philipp Leitner, Sebastian Wagner, Stefan Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Wels Austria Josef Ressel Center for Adaptive Optimization in Dynamic Environments University of Applied Sciences Upper Austria Wels Austria

ISBN: (纸本)9783031829512

In dynamic environments, solutions may become invalid during or after the execution of an optimization algorithm due to changing circumstances, requiring the adaptation or creation of new solutions. This paper uses the hot-storage area of a steel processing plant as a dynamic environment, where the optimization task involves creating lot-assignments of steel slabs for efficient transportation. The lot-assignment problem is represented as a search tree, where slabs are gradually assigned to new or existing lots as the tree is traversed. Unforeseen events in the dynamic hot-storage area can render existing lot-assignments invalid or make them less effective. Instead of completely re-running a tree-search algorithm in response to such dynamic events, we propose a backtracking strategy that uses information from outdated solutions. This strategy involves constructing a backtracking chain to check the validity of the lot-assignments used to construct the outdated solution and to identifying potential restart points for partial search spaces. Through experiments, we show the performance and reliability of various tree-search algorithms under different runtime budgets, with and without backtracking-based restart strategies. Our results strongly suggest that using information from a previous solution significantly enhances algorithm performance to identify valid solutions compared to a complete rerun. Additionally, using backtracking within the same runtime budget as a complete rerun can also improve solution quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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Online Machine Learning for the Estimation of Process Times in Dynamic Scheduling

Online Machine Learning for the Estimation of Process Tim...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Heckmann, Michael Werth, Bernhard Karder, Johannes Wagner, Stefan Josef Ressel Center for Adaptive Optimization in Dynamic Environments Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg4232 Austria Symbolic Artificial Intelligence Johannes Kepler University Linz4040 Austria

ISBN: (纸本)9783031838873

This paper presents a study on applying online machine-learning models to estimate the processing times of different production tasks for dynamic scheduling problems. Specifically, various machine-learning approaches and their impact on the schedule quality are evaluated. A discrete event simulation of a production process was created, with different functions determining processing time. This production simulation was optimized with the OERAPGA optimization algorithm. Based on the experiments, the performance of different machine learning models was assessed. Moreover, the speed and prediction quality of these models and their resulting optimization quality were evaluated. Results showed that the speed of evaluation plays a more significant role in optimization quality than prediction accuracy, as the optimizer seems to be focused on optimizing macroscopic aspects of the production schedule. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computer control

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Diversity Management in evolutionary Dynamic Optimization

Diversity Management in Evolutionary Dynamic Optimization

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Werth, Bernhard Karder, Johannes Wagner, Stefan Affenzeller, Michael Josef Ressel Center for Adaptive Optimization in Dynamic Environments Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg4232 Austria Institute for Symbolic Artificial Intelligence Johannes Kepler University Linz4040 Austria

ISBN: (纸本)9783031829512

The retention of diversity of genetic information is an important aspect of many population-based evolutionary optimizers. With the increasing relevance of dynamic optimization, where live data is streamed directly into a running optimization system, this algorithmic facet gains new importance. This study compares five different strategies for handling diversity in genetic algorithms in a dynamic open-ended optimization scenario. Using the traveling salesman problem as a benchmark, the algorithmic variations are compared and analyzed with respect to their performance and retained diversity. Results indicate that convergence patterns behave differently from static optimization and several algorithm features that are well understood for static optimization may have unintended consequences in dynamic scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic algorithms

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Concurrent Evolution of Dynamic Single and Dual-Crane Scheduling Scenarios

Concurrent Evolution of Dynamic Single and Dual-Crane Sche...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Karder, Johannes Werth, Bernhard Wagner, Stefan Affenzeller, Michael Josef Ressel Center for Adaptive Optimization in Dynamic Environments Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg4232 Austria Institute for Symbolic Artificial Intelligence Johannes Kepler University Linz4040 Austria

ISBN: (纸本)9783031838873

Various approaches can be used to solve dynamic optimization problems. For example, on the one hand, optimization algorithms can be restarted every time the problem changes. As this results in a loss of optimization progress, algorithms can on the other hand also be implemented in an open-ended way, and to adapt to changing problem data during the run. Some problem updates cause fundamental changes to the optimization scenario. This paper describes different strategies to evolve solutions for such problems with scenario changes in the context of crane scheduling operations. It shows that simply ignoring such changes has negative effects on optimizer convergence, and compares the convergence behavior of five different strategies that can be applied when switches between different scenarios occur. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dynamic programming

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