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36th European Modeling and Simulation Symposium, EMSS 2024, Held at the 21st International Multidisciplinary Modeling and Simulation Multiconference, I3M 2024

作者： Werth, Bernhard Karder, Johannes Wagner, Stefan Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Institute for Symbolic Artificial Intelligence Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria

ISBN: (纸本)9791281988026

Efficient Global Optimization (EGO) is a very important black-box optimization framework for solving expensive optimization problems, which appear in high-fidelity simulation-based optimization. EGO operates by employing a Gaussian process model as an approximation to the computationally expensive black-box function. As we observe an increasing trend towards dynamic optimization problems that incorporate live data, combined with the fact that EGO is designed to solve static optimization problems, a need for adaptation arises. This paper analyzes and compares five EGO extensions that should aid the algorithm in dealing with dynamic changes. Results indicate that such dynamification is challenging and while successful for problem instances with the correct difficulty and change severity, it can have adverse effects if older data is unrepresentative of new scenarios. © 2024 The Authors.

关键词： Optimization algorithms

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A Novel Benchmark Environment for Dynamic Factory Crane Scheduling 4

A Novel Benchmark Environment for Dynamic Factory Crane Sche...

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4th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2022

作者： Karder, Johannes Werth, Bernhard Beham, Andreas Wagner, Stefan Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Institute for Symbolic Artificial Intelligence Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria

In dynamic warehouse operations, factory crane scheduling is a challenging problem to be solved. The interplay between a number of cranes requires good coordination to efficiently handle requested transport orders within the warehouse. The objective is to maximize the throughput and therefore minimize the makespan of crane schedules for a given set of transport orders and resulting crane moves. However, in real world processes, uncertainty is a key factor that needs to be considered. New transport orders arrive without prior announcement during the planning window, as the exact timing of vehicles responsible for inbound and outbound flows are often available only on short notice. Furthermore, events such as crane maintenance or operator breaks must be dealt with accordingly. To further the algorithmic research where these challenging events in the domain of dynamic factory crane scheduling must be tackled, we propose an open-source simulation model that serves as a novel benchmark. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Cranes

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Analysis and Handling of Dynamic Problem Changes in Open-Ended Optimization 18th

Analysis and Handling of Dynamic Problem Changes in Open-...

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18th International Conference on Computer Aided Systems Theory, EUROCAST 2022

作者： Karder, Johannes Werth, Bernhard Beham, Andreas 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)9783031253119

Changes in dynamic optimization problems entail updates to the problem model, which in turn can result in changes to the problem’s fitness landscape and even its solution encoding. In order to yield valid solutions that are applicable to the current problem state, optimization algorithms must be able to cope with such dynamic problem updates. Furthermore, depending on the optimization use case, changes occurring in real-world environments require an optimizer to adapt to changing process conditions and yield updated, valid solutions within a short time frame. In this paper, dynamic problem changes and their effects on an optimizer’s algorithmic behavior are studied in the context of crane scheduling. Three open-ended versions of RAPGA, a relevant alleles preserving genetic algorithm, are evaluated, some of which include self-adaption and a special treatment of certain events that require domain knowledge to be recognized. The proposed extensions affect the algorithm behavior as desired. On the one hand, the algorithms converge faster after a loss in solution quality is detected. On the other hand, new genetic material is introduced, making it possible to reach high quality areas of the search space again. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Multi-objective optimization for a scheduling problem in the steel industry 31

Multi-objective optimization for a scheduling problem in the...

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31st European Modeling and Simulation Symposium, EMSS 2019

作者： Hauder, Viktoria A. Beham, Andreas Raggl, Sebastian Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria Institute for Production and Logistics Management Johannes Kepler University Linz Austria Institute for Formal Models and Verification Johannes Kepler University Linz Austria

ISBN: (纸本)9788885741263

Multiple conflicting objectives such as costs versus quality are part of many optimization processes in the area of production and logistics management. Exactly such a case is also examined in this work. For an already existing resource-constrained project scheduling problem, a second objective function, inspired by the steel industry, is taken into account. Together with the presentation of the related mixed integer programming (MIP) and constraint programming (CP) models, the recently developed balanced box method (Boland, Charkhgard, and Savelsbergh 2015) is used to solve this bi-objective optimization problem. Both approaches (MIP and CP) are compared in terms of runtime and solution quality, showing the advantages of using CP. © 2019 Dime Universita di Genova, DIMEG University of Calabria.

关键词： Multiobjective optimization

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Genetic programming with data migration for symbolic regression 14

Genetic programming with data migration for symbolic regress...

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16th Genetic and evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： Kommenda, Michael Affenzeller, Michael Burlacu, Bogdan Kronberger, Gabriel Winkler, Stephan M. University of Applied Sciences Upper Austria Heuristic and Evolutionary Algorithms Laboratory Softwarepark 11 4232 Hagenberg Austria Johannes Kepler University Linz Institute for Formal Models and Verification Altenbergerstr. 69 4040 Linz Austria

ISBN: (纸本)9781450328814

In this publication genetic programming (GP) with data migration for symbolic regression is presented. The motivation for the development of the algorithm is to evolve models which generalize well on previously unseen data. GP with data migration uses multiple subpopulations to maintain the genetic diversity during the algorithm run and a sophisticated training subset selection strategy. Each subpopulation is evaluated on a different fixed training subset (FTS) and additionally a variable training subset (VTS) is exchanged between the subpopulations at specific data migration intervals. Thus, the individuals are evaluated on the unification of FTS and VTS and should have better generalization properties due to the regular changes of the VTS. The implemented algorithm is compared to several GP variants on a number of symbolic regression benchmark problems to test the effectiveness of the multiple populations and data migration strategy. Additionally, different algorithm configurations and migration strategies are evaluated to show their impact with respect to the achieved quality.

关键词： Genetic programming

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Utilizing Interpretable Machine Learning to detect Dynamics in Energy Communities 34

Utilizing Interpretable Machine Learning to detect Dynamics ...

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34th European Modeling and Simulation Symposium, EMSS 2022

作者： Zenisek, Jan Bachinger, Florian Pitzer, Erik Wagner, Stefan Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Institute for Symbolic Artificial Intelligence Johannes Kepler University Altenberger Straße 69 Linz4040 Austria

With the growing use of machine learning models in many critical domains, research regarding making these models, as well as their predictions, more explainable has intensified in the last few years. In this paper, we present extensions to the machine learning based data mining technique Variable Interaction Networks (VIN), to integrate existing domain knowledge and thus, enable more meaningful analysis. Several tests on data from a case study concerned with long-term monitored photovoltaic systems, verify the feasibility of our approach to provide valuable, human-interpretable insights. In particular, we show the successful application of root-cause detection in scenarios with changing system conditions. © 2022 The Authors.

关键词： Machine learning

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heuristic optimization methods for the tuning of input parameters of simulation models

Heuristic optimization methods for the tuning of input param...

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4th International Mediterranean Modeling Multiconference, I3M 2007

作者： Affenzeller, Michael Kronberger, Gabriel Winkler, Stephan Ionescu, Mihaela Wagner, Stefan Upper Austrian University of Applied Sciences Campus Hagenberg Heuristic and Evolutionary Algorithms Laboratory Softwarepark 11 A-4232 Hagenberg Austria Johannes Kepler University Linz Institute of Bioinformatics Altenbergerstr. 69 A-4040 Linz Austria

ISBN: (纸本)8890073268

This paper proposes an approach combining evolutionary Computation and Simulation: An extended problem representation as well as solution manipulation concepts for Evolution Strategies are proposed with the aim to fulfill the needs of input parameter optimization of simulation models;moreover, this approach is not restricted to real-valued parameters. The usage of a parallel infrastructure is also proposed and planned to be realized based on the heuristicLab framework for heuristic optimization.

关键词： heuristic methods

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Scripting and framework integration in heuristic optimization environments 14

Scripting and framework integration in heuristic optimizatio...

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16th Genetic and evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： Beham, Andreas Karder, Johannes Kronberger, Gabriel Wagner, Stefan Kommenda, Michael Scheibenpflug, Andreas University of Applied Sciences Upper Austria Heuristic and Evolutionary Algorithms Laboratory Softwarepark 11 4232 Hagenberg Austria Johannes Kepler University Linz Institute for Formal Models and Verification Altenberger Straße 69 4040 Linz Austria

ISBN: (纸本)9781450328814

Rapid prototyping and testing of new ideas has been a major argument for evolutionary computation frameworks. These frameworks facilitate the application of evolutionary computation and allow experimenting with new and modified algorithms and problems by building on existing, well tested code. However, one could argue, that despite the many frameworks of the metaheuristics community, software packages such as MATLAB, GNU Octave, Scilab, or RStudio are quite popular. These software packages however are associated more closely with numerical analysis rather than evolutionary computation. In contrast to typical evolutionary computation frameworks which provide standard implementations of algorithms and problems, these popular frameworks provide a direct programming environment for the user and several helpful functions and mathematical operations. The user does not need to use traditional development tools such as a compiler or linker, but can implement, execute, and visualize his ideas directly within the environment. heuristicLab has become a popular environment for heuristic optimization over the years, but has not been recognized as a programming environment so far. In this article we will describe new scripting capabilities created in heuristicLab and give information on technical details of the implementation. Additionally, we show how the programming interface can be used to integrate further metaheuristic optimization frameworks in heuristicLab.

关键词： evolutionary algorithms

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Novel robustness measures for engineering design optimisation

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International Journal of Simulation and Process Modelling 2018年第4期13卷 387-401页

作者： Fleck, Philipp Kommenda, Michael Prante, Thorsten Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg im Mühlkreis4232 Austria Institute for Formal Models and Verification Johannes Kepler University Linz Altenbergerstraße 69 Linz4040 Austria Design Automation V-Research GmbH Stadtstraße 33 Dorinbirn6850 Austria

This paper presents novel robustness measures to analyse and compare the robustness of solutions for constrained optimisation problems in the field of engineering design optimisation. First, we define uncertainty in production processes and present a method to quantify uncertainty. Based on the variations of a solution that are introduced by uncertainty, we want to assess the robustness of those solutions towards those variations. We show how a solution's quality and feasibility (with regards to constraint violation) change with increasing uncertainty and discuss how those changes determine the robustness of that solution. Furthermore, we present a method of aggregating that information into a single, real-valued robustness measure. This novel robustness measure can be used to select solutions that have a high robustness along with a high quality. To test the presented measures extensively, we apply them to various solutions for benchmark problems from published literature in the field of engineering design optimisation. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Constrained optimization

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Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data

arXiv

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arXiv 2022年

作者： Burlacu, Bogdan Kommenda, Michael Kronberger, Gabriel Winkler, Stephan Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria

Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which allow to calculate the potential energy of an atomic system as a function of atomic coordinates and potentially other properties. First-principles-based ab initio potentials can reach arbitrary levels of accuracy, however their aplicability is limited by their high computational cost. Machine learning (ML) has recently emerged as an effective way to offset the high computational costs of ab initio atomic potentials by replacing expensive models with highly efficient surrogates trained on electronic structure data. Among a plethora of current methods, symbolic regression (SR) is gaining traction as a powerful "white-box" approach for discovering functional forms of interatomic potentials. This contribution discusses the role of symbolic regression in Materials Science (MS) and offers a comprehensive overview of current methodological challenges and state-of-the-art results. A genetic programming-based approach for modeling atomic potentials from raw data (consisting of snapshots of atomic positions and associated potential energy) is presented and empirically validated on ab initio electronic structure data. © 2022, CC BY.

关键词： Potential energy

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