the proceedings contain 24 papers from the evolutionarycomputation in combinatorialoptimization - 5theuropeanconference, EvoCOP 2005, Proceedings. the topics discussed include: an external partial permutations mem...
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the proceedings contain 24 papers from the evolutionarycomputation in combinatorialoptimization - 5theuropeanconference, EvoCOP 2005, Proceedings. the topics discussed include: an external partial permutations memory for ant colony optimization;a novel application of evolutionary computing in process systems engineering;on the application of evolutionary algorithms to the consensus tree problem;immune algorithms with aging operators for the string folding problem and the protein folding problem;lot-sizing in a foundry using genetic algorithm and repair functions;estimation of distribution algorithms with mutation;application of the grouping genetic alogrithm to university course timetabling.
the proceedings contain 16 papers. the special focus in this conference is on evolutionarycomputation in combinatorialoptimization. the topics include: evolutionary Anytime Algorithms;studies on Survival Strate...
ISBN:
(纸本)9783031868481
the proceedings contain 16 papers. the special focus in this conference is on evolutionarycomputation in combinatorialoptimization. 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.
the proceedings contain 13 papers. the special focus in this conference is on evolutionarycomputation in combinatorialoptimization. the topics include: Clarifying the difference in local optima network sampling algo...
ISBN:
(纸本)9783030167103
the proceedings contain 13 papers. the special focus in this conference is on evolutionarycomputation in combinatorialoptimization. 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.
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 adaptab...
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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.
the proceedings contain 19 papers. the special focus in this conference is on evolutionarycomputation in combinatorialoptimization. the topics include: A biased random-key genetic algorithm for the cloud resource ma...
ISBN:
(纸本)9783319164670
the proceedings contain 19 papers. the special focus in this conference is on evolutionarycomputation in combinatorialoptimization. the topics include: A biased random-key genetic algorithm for the cloud resource management problem;a new solution representation for the firefighter problem;a variable neighborhood search approach for the interdependent lock scheduling problem;a variable neighborhood search for the generalized vehicle routing problem with stochastic demands;an iterated local search algorithm for solving the orienteering problem with time windows;analysis of solution quality of a multiobjective optimization-based evolutionary algorithm for knapsack problem;evolving deep recurrent neural networks using ant colony optimization;mixing network extremal optimization for community structure detection;multi-start iterated local search for the mixed fleet vehicle routing problem with heterogenous electric vehicles;the sim-EA algorithm with operator autoadaptation for the multiobjective firefighter problem and using local search to evaluate dispatching rules in dynamic job shop scheduling.
the proceedings contain 12 papers. the special focus in this conference is on evolutionarycomputation in combinatorialoptimization. the topics include: Emergence of New Local Search Algorithms with Neuro-E...
ISBN:
(纸本)9783031577116
the proceedings contain 12 papers. the special focus in this conference is on evolutionarycomputation in combinatorialoptimization. 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.
the proceedings contain 12 papers. the special focus in this conference is on evolutionarycomputation in combinatorialoptimization. the topics include: A multistart alternating tabu search for commercial districting...
ISBN:
(纸本)9783319774480
the proceedings contain 12 papers. the special focus in this conference is on evolutionarycomputation in combinatorialoptimization. the topics include: A multistart alternating tabu search for commercial districting;an ant colony approach for the winner determination problem;on the fractal nature of local optima networks;How perturbation strength shapes the global structure of TSP fitness landscapes;worst improvement based iterated local search;automatic grammar-based design of heuristic algorithms for unconstrained binary quadratic programming;automatic algorithm configuration for the permutation flow shop scheduling problem minimizing total completion time;data clustering using grouping hyper-heuristics;reference point adaption method for genetic programming hyper-heuristic in many-objective job shop scheduling;MOEA/DEP: An algebraic decomposition-based evolutionary algorithm for the multiobjective permutation flowshop scheduling problem.
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