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25th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2025 Held as Part of EvoStar 2025

25th European Conference on Evolutionary Computation in Comb...

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25th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2025 Held as Part of EvoStar 2025

ISBN: (纸本)9783031868481

the proceedings contain 16 papers. the special focus in this conference is on evolutionary computation in combinatorial optimization. the topics include: evolutionary Anytime Algorithms;studies on Survival Strategies to Protect Expert Knowledge in evolutionary Algorithms for Interactive Role Mining;diversification through Candidate Sampling for a Non-iterated Lin-Kernighan-Helsgaun Algorithm;instance Space Analysis and Algorithm Selection for a Parallel Batch Scheduling Problem;meta-learning of Univariate Estimation-of-Distribution Algorithms for Pseudo-Boolean Problems;a Selective Vehicle Routing Problem for the Bloodmobile System;a Genetic Approach to the Operational Freight-on-Transit Problem;LON/D — Sub-problem Landscape Analysis in Decomposition-Based Multi-objective optimization;visualizing Pseudo-Boolean Functions: Feature Selection and Regularization for Machine Learning;mixed-Binary Problems Optimized with Fast Discrete Solver;feature-Based evolutionary Diversity optimization of Discriminating Instances for Chance-Constrained optimization Problems;Adaptive Neighborhood Search Based on Landscape Learning: A TSP Study;healthcare Facility Location Problem and Fitness Landscape Analysis;generating (Semi-)active Schedules for Dynamic Multi-mode Project Scheduling Using Genetic Programming Hyper-heuristics.

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24th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2024, held as part of EvoStar 2024

24th European Conference on Evolutionary Computation in Comb...

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24th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2024, held as part of EvoStar 2024

ISBN: (纸本)9783031577116

the proceedings contain 12 papers. the special focus in this conference is on evolutionary computation in combinatorial optimization. the topics include: Emergence of New Local Search Algorithms with Neuro-Evolution;q-Learning Based Framework for Solving the Stochastic E-waste Collection Problem;a Memetic Algorithm with Adaptive Operator Selection for Graph Coloring;Studies on Multi-objective Role Mining in ERP Systems;greedy Heuristic Guided by Lexicographic Excellence;Reduction-Based MAX-3SAT with Low Nonlinearity and Lattices Under Recombination;Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT Instances;hardest Monotone Functions for evolutionary Algorithms;a theoretical Investigation of Termination Criteria for evolutionary Algorithms;Experimental and theoretical Analysis of Local Search Optimising OBDD Variable Orderings.

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20th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2020, held as part of Evostar 2020

20th European Conference on Evolutionary Computation in Comb...

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20th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2020, held as part of Evostar 2020

ISBN: (纸本)9783030436797

the proceedings contain 14 papers. the special focus in this conference is on evolutionary computation in combinatorial optimization. the topics include: MILPIBEA: Algorithm for Multi-objective Features Selection in (Evolving) Software Product Lines;a Group Genetic Algorithm for Resource Allocation in Container-Based Clouds;the Local Optima Level in Chemotherapy Schedule Optimisation;genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling;an Algebraic Approach for the Search Space of Permutations with Repetition;a Comparison of Genetic Representations for Multi-objective Shortest Path Problems on Multigraphs;the Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis;a Beam Search Approach to the Traveling Tournament Problem;Cooperative Parallel SAT Local Search with Path Relinking;dynamic Compartmental Models for Large Multi-objective Landscapes and Performance Estimation;fitness Landscape Analysis of Automated Machine Learning Search Spaces;On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D.

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A theoretical Investigation of Termination Criteria for evolutionary Algorithms 24th

A Theoretical Investigation of Termination Criteria for Evol...

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24th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Rowe, Jonathan E. Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)9783031577116;9783031577123

We take a theoretical approach to analysing conditions for terminating evolutionary algorithms. After looking at situations where much is known about the particular algorithm and problem class, we consider a more generic approach. Schemes that depend purely on the previous time to improvement are shown not to work. An alternative criterion, the lambda-parallel scheme, does terminate correctly (with high probability) for any randomised search heuristic algorithm on any problem, provided certain conditions on the improvement probabilities are met. A more natural and less costly approach is then presented based on the run-time so far. this is shown to work for the classes of monotonic and path problems (for Randomised Local Search). It remains an open question whether it works in a more general setting.

关键词： evolutionary algorithms

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Coloring Dynamic Graphs With a Similarity and Pool-Based evolutionary Algorithm

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IEEE ACCESS 2025年 13卷 38054-38075页

作者： Terci, Gizem Sungu Boz, Betul Gebze Tech Univ Inst Informat Technol TR-41400 Kocaeli Turkiye Marmara Univ Comp Engn Dept TR-34854 Istanbul Turkiye

the graph coloring problem is a well-known optimization challenge, particularly relevant in dynamic environments where the graph undergoes continuous changes over time. evolutionary algorithms, known for their adaptability and effectiveness in handling NP-hard problems, are well-suited for tackling the issues related to coloring dynamic graphs. In this paper, we present a novel Similarity and Pool-Based evolutionary Algorithm designed to address the graph coloring problem on dynamic graphs. Our approach employs a partition-based representation that adapts to dynamic graph changes while preserving valuable historical information. the algorithm introduces an innovative similarity and conflict-based crossover operator aimed at minimizing the number of colors used, alongside a local search method to enhance solution diversity. We evaluated the performance of the proposed algorithm against a well-known heuristic for the graph coloring problem and a genetic algorithm with a dynamic population across a diverse set of dynamic graphs. Experimental results demonstrate that our algorithm consistently outperforms these alternatives by reducing the number of colors required in the majority of test cases.

关键词： Heuristic algorithms Color evolutionary computation Social networking (online) Real-time systems Partitioning algorithms Adaptation models Metaheuristics Genetic operators Communication networks Dynamic graphs edge-dynamic graphs node-dynamic graphs graph coloring problem crossover operator evolutionary algorithms

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Greedy Heuristic Guided by Lexicographic Excellence 24th

Greedy Heuristic Guided by Lexicographic Excellence

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24th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Tamby, Satya Gourves, Laurent Moretti, Stefano Univ PSL Univ Paris Dauphine CNRS LAMSADE F-75016 Paris France

ISBN: (纸本)9783031577116;9783031577123

this article deals with a basic greedy algorithm which, element by element, is able to construct a feasible solution to a wide family of combinatorial optimization problems. the novelty is to guide the greedy algorithm by considering the elements of the problem by order of merit, following a social ranking method. Social rankings come from social choice theory. the method used in the present article, called lexicographic excellence, sorts individual elements on the basis of the performances of groups of elements. In order to validate our approach, we conduct a theoretical analysis on matroid optimization problems, followed by a thorough experimental study on the multi-dimensional knapsack and the maximum weight independent set problem, leading to promising results.

关键词： combinatorial optimization Greedy heuristic Social ranking Lexicographic excellence

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Sparse Surrogate Model for optimization: Example of the Bus Stops Spacing 24th

Sparse Surrogate Model for Optimization: Example of the Bus ...

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24th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Vendi, Valentin Verel, Sebastien Fonlupt, Cyril Univ Littoral Cote dOpale LISIC F-62100 Calais France

ISBN: (纸本)9783031577116;9783031577123

combinatorial optimization problems can involve computationaly expensive fitness function, making their resolution challenging. Surrogate models are one of the effective techniques used to solve such black-box problems by guiding the search towards potentially good solutions. In this paper, we focus on the use of surrogate based on multinomial approaches, particularly based onWalsh functions, to tackle pseudo-Boolean problems. Although this approach can be effective, a potential drawback is the growth of the polynomial expansion with problem dimension. We introduce a method for analyzing real-world combinatorial black-box problems defined through numerical simulation. this method combines Walsh spectral analysis and polynomial regression. Consequently, we propose a sparse surrogate model that incorporates selected, relevant terms and is simpler to optimize. To demonstrate our approach, we apply it to the bus stop spacing problem, an exemplary combinatorial pseudo-Boolean challenge.

关键词： Surrogate Sparse model Mobility problem Walsh basis

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Studies on Multi-objective Role Mining in ERP Systems 24th

Studies on Multi-objective Role Mining in ERP Systems

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24th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Anderer, Simon Scheuermann, Bernd Mostaghim, Sanaz SIVIS GmbH Grunhutstr 6 D-76187 Karlsruhe Germany Karlsruhe Univ Appl Sci D-76133 Karlsruhe Germany Otto von Guericke Univ Magdeburg Germany

ISBN: (纸本)9783031577116;9783031577123

A common concept to ensure the security of IT systems, in which multiple users share access to common resources, is Role Based Access Control (RBAC). Permissions, which correspond to the authorization to perform an operation on a data or business object are grouped into roles. these roles are then assigned to users. the corresponding optimization problem, the so-called Role Mining Problem (RMP), aims at finding a role concept comprising a minimal set of such roles and was shown to be NP-complete. However, in real-world role mining scenarios, it is typically the case that, besides the number of roles, further key figures must be consulted in order to adequately evaluate role concepts. therefore, in this paper, the RMP is extended to a multi-objective (MO) optimization problem. Potential optimization objectives are discussed in the context of Enterprise Resource Planning (ERP) systems. Furthermore, it is shown, how evolutionary algorithms for the RMP can be adapted to meet the requirements of MO role mining. Based on this, the integration of different optimization objectives is examined and evaluated in a series of experiments.

关键词： Role based access control Enterprise resource planning systems evolutionary algorithms Multi-objective optimization

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19th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2019, held as part of EvoStar 2019

19th European Conference on Evolutionary Computation in Comb...

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19th european conference on evolutionary computation in combinatorial optimization, EvoCOP 2019, held as part of EvoStar 2019

ISBN: (纸本)9783030167103

the proceedings contain 13 papers. the special focus in this conference is on evolutionary computation in combinatorial optimization. the topics include: Clarifying the difference in local optima network sampling algorithms;a unifying view on recombination spaces and abstract convex evolutionary search;program Trace optimization with Constructive Heuristics for combinatorial Problems;a binary algebraic differential evolution for the multidimensional two-way number partitioning problem;a new representation in genetic programming for evolving dispatching rules for dynamic flexible job shop scheduling;an iterated local search algorithm for the two-machine flow shop problem with buffers and constant processing times on one machine;route planning for a fleet of electric vehicles with waiting times at charging stations;multiple periods vehicle routing problems: A case study;Rigorous performance analysis of state-of-the-art TSP heuristic solvers;runtime analysis of discrete particle swarm optimization applied to shortest paths computation;quasi-optimal recombination operator.

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Emergence of New Local Search Algorithms with Neuro-Evolution 24th

Emergence of New Local Search Algorithms with Neuro-Evolutio...

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24th european conference on evolutionary computation in combinatorial optimization (EvoCOP)

作者： Goudet, Olivier Sakhri, Mohamed Salim Amri Goeffon, Adrien Saubion, Frederic Univ Angers LERIA SFR MATHSTIC F-49000 Angers France

ISBN: (纸本)9783031577116;9783031577123

this paper explores a novel approach aimed at overcoming existing challenges in the realm of local search algorithms. the main objective is to better manage information within these algorithms, while retaining simplicity and generality in their core components. Our goal is to equip a neural network with the same information as the basic local search and, after a training phase, use the neural network as the fundamental move component within a straightforward local search process. To assess the efficiency of this approach, we develop an experimental setup centered around NK landscape problems, offering the flexibility to adjust problem size and ruggedness. this approach offers a promising avenue for the emergence of new local search algorithms and the improvement of their problem-solving capabilities for black-box problems.

关键词： Neuro-evolution Local search Black-box optimization NK landscapes

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