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检索条件"任意字段=11th European Conference on Evolutionary Computation in Combinatorial Optimization"

共 291 条记录，以下是11-20 订阅

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Reduction-Based MAX-3SAT with Low Nonlinearity and Lattices Under Recombination 24th

Reduction-Based MAX-3SAT with Low Nonlinearity and Lattices ...

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

作者： Whitley, Darrell Ochoa, Gabriela Floyd, Noah Chicano, Francisco Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA Univ Stirling Comp Sci & Math Stirling Scotland Univ Malaga ITIS Software Malaga Spain

ISBN: (纸本)9783031577116;9783031577123

A new construction is introduced for creating random MAX3SAT instances with low nonlinearity. Instead of generating random clauses, we generate random SAT expressions over 3 variables and then convert these into CNF SAT clauses. We prove that this yields structured problems with much lower nonlinearity. We also introduce a new method for weighting MAX-SAT clauses that preserves low nonlinearity and also breaks up plateaus. We evaluate these new problems by enumeration of instances with n = 30 variables. One unexpected result is that Partition Crossover creates more tunnels on these semi-structured MAX-SAT problems compared to results on random NK landscapes. We show that Partition Crossover induces hypercube lattices over subsets of local optima;all of the local optima which appear in a lattice can be evaluated with a single linear equation.

关键词： MAX-SAT Recombination Operators Partition Crossover evolutionary combinatorial optimization

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Hardest Monotone Functions for evolutionary Algorithms 24th

Hardest Monotone Functions for Evolutionary Algorithms

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

作者： Kaufmann, Marc Larcher, Maxime Lengler, Johannes Sieberling, Oliver Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9783031577116;9783031577123

the hardness of optimizing monotone functions using the (1 + 1)-EA has been an open problem for a long time. By introducing a more pessimistic stochastic process, the partially-ordered evolutionary algorithm (PO-EA) model, Jansen proved a runtime bound of O(n(3/2)). In 2019, Lengler, Martinsson and Steger improved this upper bound to O(n log 2 n) leveraging an entropy compression argument. We continue this line of research by analyzing monotone functions that may vary at each step, so-called dynamic monotone functions. We introduce the function Switching Dynamic BinVal (SDBV) and prove, using a combinatorial argument, that for the (1 + 1)-EA, SDBV is drift minimizing within the class of dynamic monotone functions. We further show that the (1 + 1)-EA optimizes SDBV in theta(n(3/2)) generations. therefore, our construction provides the first explicit example which realizes the pessimism of the PO-EA model. Our simulations demonstrate matching runtimes for both static and self-adjusting (1,lambda) and (1 +lambda)-EA. We additionally demonstrate, devising an example of fixed dimension, that drift minimization does not equal maximal runtime.

关键词： hardest functions fitness landscape precise runtime analysis drift minimization (1+1)-EA Switching Dynamic Binary Value dynamic environments evolutionary algorithm

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A Relaxation Oscillator -Based Probabilistic combinatorial optimization Engine for Soft Decoding of LDPC Codes 50

A Relaxation Oscillator -Based Probabilistic Combinatorial O...

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50th IEEE european Solid-State Electronics Research conference (ESSERC)

作者： Dikopoulos, Evangelos Wormald, Luke Hsu, Ying-Tuan Tang, Wei Chen, Po-Shao Zhang, Zhengya Flynn, Michael P. Univ Michigan Elect Engn & Comp Sci Ann Arbor MI 48109 USA

ISBN: (纸本)9798350388138;9798350388145

Drawing insights from quantum computing, oscillator-based computing leverages continuous-time operation and massive parallelism to accelerate challenging computational tasks. this work advances the field to demonstrate a combinatorial optimization Problem (COP) engine for efficient, robust, one-shot oscillator-based soft decoding of LDPC codes for the first time. We present a 28nm CMOS prototype that achieves a Frame, Bit Error Rate (FER, BER) of 1.38 x 10(-5), 1.25 x 10(-6) respectively at 7dB SNR and an energy efficiency of 5.26 pJ/bit, which surpasses the normalized efficiencies of recent state-of-the-art decoders [1][2][3] by 11x, 3x, 1.5x respectively. Tested with more than 100 million frame decodings, the prototype demonstrates consistent performance across a range of SNRs, supply voltages, and temperatures.

关键词： combinatorial optimization relaxation-oscillator soft-information LDPC oscillator-based computation

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Mixed-Binary Problems Optimized with Fast Discrete Solver 25th

Mixed-Binary Problems Optimized with Fast Discrete Solver

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

作者： Kötzing, Timo Radhakrishnan, Aishwarya Hasso Plattner Institute University of Potsdam Potsdam Germany

ISBN: (纸本)9783031868481

Tackling optimization in mixed domains (continuous and discrete decision variables) has recently gained attention, causing the development of various extensions of continuous optimization algorithms. In order to more accurately address the combinatorial nature of the discrete aspects of the search space, we go a different way and combine two algorithms, one for each aspect of the search space. Focusing, in the discrete part, on binary search spaces, we use Population-Based Incremental Learning (PBIL) for discrete optimization. We combine this algorithm with the Covariance Matrix Adaptation evolutionary Strategy (CMA-ES) to address the continuous part. We compare our CMA-ES-PBIL with two leading variants of CMA-ES from the literature: CMA-ES with Margin (CMA-ESwM) and CMA-ES with Probability Distribution Model (CMA-ES-PDM). We conduct run time analysis on some separable and a non-separable benchmark functions and show that our hybrid algorithm significantly outperforms both CMA-ESwM and CMA-ES-PDM. Our results also show that the binary part is optimized much earlier than the continuous part, raising the question: Is run time evaluation the right measure to analyze mixed-binary algorithms? © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optimization algorithms

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evolutionary Diversity optimization for combinatorial optimization 22

Evolutionary Diversity Optimization for Combinatorial Optimi...

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Genetic and evolutionary computation conference (GECCO)

作者： Bossek, Jakob Neumann, Aneta Neumann, Frank Rhein Westfal TH Aachen Dept Comp Sci AI Methodol AIM Aachen Germany Univ Adelaide Sch Comp Sci Optimisat & Logist Grp Adelaide SA Australia

• Majority of approaches consider diversity in obj. space.• Ulrich/thiele [UT11] considered diversity in search space (see, e. g., Tamara Ulrich's PhD thesis [Ulr12])• Diversity with respect to other properties (f... 详细信息

ISBN: (纸本)9781450392686

关键词： combinatorial optimization

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Instance Space Analysis and Algorithm Selection for a Parallel Batch Scheduling Problem 25th

Instance Space Analysis and Algorithm Selection for a Par...

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

作者： Da Ros, Francesca Di Gaspero, Luca Lackner, Marie-Louise Musliu, Nysret Università degli Studi di Udine Udine Italy Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling TU Wien Vienna Austria

ISBN: (纸本)9783031868481

this paper addresses the Oven Scheduling Problem (OSP), a parallel batch scheduling problem in semiconductor manufacturing, and identifies strengths and weaknesses of solution methods using the Instance Space Analysis (ISA) methodology. We propose a comprehensive feature set to effectively characterize OSP instances and generate more diverse instances compared to the literature. the performance of two state-of-the-art algorithms for the OSP – Simulated Annealing and Large Neighborhood Search – is analyzed using ISA, revealing distinct regions of superior or inferior performance for each, as well as areas of equal performance. Finally, we propose an automated algorithm selection approach that outperforms any single algorithm. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Simulated annealing

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Studies on Survival Strategies to Protect Expert Knowledge in evolutionary Algorithms for Interactive Role Mining 25th

Studies on Survival Strategies to Protect Expert Knowledge...

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

作者： Anderer, Simon Justen, Nicolas Scheuermann, Bernd Mostaghim, Sanaz Pointsharp GmbH Grünhutstraße 6 Karlsruhe76187 Germany Karlsruhe University of Applied Sciences Karlsruhe76133 Germany Otto-von-Guericke-Universität Magdeburg Magdeburg Germany

ISBN: (纸本)9783031868481

To maintain the integrity of information technology infrastructures within enterprises and organizations, it is imperative to implement robust and dependable access control mechanisms. A prevalent method is Role-Based Access Control (RBAC), wherein permissions are groped into roles, which are subsequently assigned to the users of an IT system. Such assignments are referred to as role concepts. the objective of the Role Mining Problem (RMP), whose decision variant is NP-complete, is to find a role concept with minimal number of roles and evolutionary algorithms have been applied as effective meta-heuristic techniques to approximate optimal solutions. Recent studies have demonstrated that the integration of expert knowledge through user interaction with running evolutionary algorithms can substantially enhance the optimization process. In this paper, expert knowledge is integrated into a evolutionary role mining algorithm by injecting favorable roles into the individuals representing role concepts. the impact of such role injections on the optimization progress is investigated and several survival strategies are presented to ensure that individuals with injected roles survive long enough to exert their beneficial effect on the optimization process. the proposed survival strategies are evaluated in a series of experiments. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anthropogenic

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Feature-Based evolutionary Diversity optimization of Discriminating Instances for Chance-Constrained optimization Problems 25th

Feature-Based Evolutionary Diversity Optimization of Discri...

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

作者： Ahouei, Saba Sadeghi Antipov, Denis Neumann, Aneta Neumann, Frank Optimisation and Logistics School of Computer and Mathematical Sciences The University of Adelaide AdelaideSA Australia LIP6 Sorbonne University Paris France

ISBN: (纸本)9783031868481

Algorithm selection is crucial in the field of optimization, as no single algorithm performs perfectly across all types of optimization problems. Finding the best algorithm among a given set of algorithms for a given problem requires a detailed analysis of the problem’s features. To do so, it is important to have a diverse set of benchmarking instances highlighting the difference in algorithms’ performance. In this paper, we evolve diverse benchmarking instances for chance-constrained optimization problems that contain stochastic components characterized by their expected values and variances. these instances clearly differentiate the performance of two given algorithms, meaning they are easy to solve by one algorithm and hard to solve by the other. We introduce a (μ+1)EA for feature-based diversity optimization to evolve such differentiating instances. We study the chance-constrained maximum coverage problem with stochastic weights on the vertices as an example of chance-constrained optimization problems. the experimental results demonstrate that our method successfully generates diverse instances based on different features while effectively distinguishing the performance between a pair of algorithms. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optimization algorithms

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LON/D — Sub-problem Landscape Analysis in Decomposition-Based Multi-objective optimization 25th

LON/D — Sub-problem Landscape Analysis in Decomposition-Ba...

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

作者： Liefooghe, Arnaud Ochoa, Gabriela Verel, Sébastien LISIC Université du Littoral Côte d’Opale Calais France University of Stirling United Kingdom

ISBN: (纸本)9783031868481

We explore the underlying difficulties of sub-problems arising from decomposition in multi-objective optimization. Decomposition algorithms, such as MOEA/D, split the original multi-objective problem into a set of single-objective sub-problems using a scalarizing function. A weighting coefficient vector defines each sub-problem. We examine the relative difficulty of these sub-problems based on their weight vector and the chosen scalar function—either weighted sum or weighted Tchebycheff. Our approach involves creating a landscape for each sub-problem and analyzing its local optima network (LON). We contribute by jointly visualizing the LONs of sub-problems, defining LON features for decomposition, and examining their interaction with problem properties and their impact on algorithm performance. An extensive experimental analysis of bi-objective NK-landscapes reveals that landscape properties depend not only on the weight vector and scalar function but also on the objectives’ intrinsic difficulty and their degree of conflict. these factors directly affect the relative performance of MOEA/D for each sub-problem. Among the landscape features explored, the size of each sub-problem’s global optimum basin of attraction showed the strongest impact on the performance of decomposition-based multi-objective optimization. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： combinatorial optimization

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OneMax Is Not the Easiest Function for Fitness Improvements 1

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23rd european conference on evolutionary computation in combinatorial Optimisation (EvoCOP) Held as Part of EvoStar conference

作者： Kaufmann, Marc Larcher, Maxime Lengler, Johannes Zou, Xun Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (数字)9783031300356

ISBN: (纸本)9783031300349;9783031300356

We study the (1 : s + 1) success rule for controlling the population size of the (1, lambda)-EA. It was shown by Hevia Fajardo and Sudholt that this parameter control mechanism can run into problems for large s if the fitness landscape is too easy. they conjectured that this problem is worst for the ONEMAX benchmark, since in some well-established sense ONEMAX is known to be the easiest fitness landscape. In this paper we disprove this conjecture. We show that there exist s and epsilon such that the self-adjusting (1, lambda)-EA with the (1 : s + 1)-rule optimizes ONEMAX efficiently when started with epsilon n zero-bits, but does not find the optimum in polynomial time on DYNAMIC BINVAL. Hence, we show that there are landscapes where the problem of the (1 : s + 1)rule for controlling the population size of the (1, lambda)-EA is more severe than for ONEMAX. the key insight is that, while ONEMAX is the easiest function for decreasing the distance to the optimum, it is not the easiest fitness landscape with respect to finding fitness-improving steps.

关键词： parameter control onemax self-adaptation (1, lambda)-EA one-fifth rule dynamic environments evolutionary algorithm

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