Evolutionary Computation in Combinatorial Optimization

Name: Evolutionary Computation in Combinatorial Optimization | 19th European Conference, EvoCOP 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24-26, 2019, Proceedings
Brand: Springer
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19th European Conference, EvoCOP 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24-26, 2019, Proceedings

Arnaud Liefooghe Luís Paquete(Herausgeber*in)

Springer (Verlag)

Erschienen am 10. April 2019

XIV, 213 Seiten

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Beschreibung

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Inhalt

Intro
Preface
Organization
Contents
A Cooperative Optimization Approach for Distributing Service Points in Mobility Applications
1 Introduction
2 The Service Point Distribution Problem
3 Related Work
4 Cooperative Optimization Algorithm
4.1 Solution Management Component
4.2 Feedback Component
4.3 Evaluation Component
4.4 Optimization Component
5 Experimental Evaluation
5.1 Benchmark Scenarios
5.2 Computational Experiments
6 Conclusion
References
A Binary Algebraic Differential Evolution for the MultiDimensional Two-Way Number Partitioning Problem
1 Introduction
2 Related Work
3 The General Scheme of MADEB
4 Algebraic Differential Mutation for the Binary Space
4.1 Abstract Algebraic Framework
4.2 Binary Algebraic Differential Mutation
4.3 Search Characteristics of the Binary Differential Mutation
5 Variable Neighborhood Descent for MDTWNPP
6 Experiments
6.1 Experimental Tuning of MADEB
6.2 Comparison with State-of-the-Art MDTWNPP Algorithms
7 Conclusions and Future Work
References
A New Representation in Genetic Programming for Evolving Dispatching Rules for Dynamic Flexible Job Shop Scheduling
1 Introduction
2 Background
2.1 Dynamic Flexible Job Shop Scheduling
2.2 Dispatching Rules in Dynamic Flexible Job Shop Scheduling
2.3 Related Work
3 The Proposed GP Approach
3.1 Representation
3.2 Components Design
4 Experiment Design
4.1 Simulation Configuration
4.2 Parameter Settings
5 Results and Discussions
5.1 Test Performance of Evolved Rules
5.2 Distribution of Average Objective Value
5.3 Rule Analyses
6 Conclusions and Future Work
References
An Iterated Local Search Algorithm for the Two-Machine Flow Shop Problem with Buffers and Constant Processing Times on One Machine
1 Introduction
2 Related Work
3 Formal Description of the Problem
4 Complexity Results
5 A Modification of the NEH Heuristic
6 Iterated Local Search
7 Computational Evaluation
7.1 Choice of Algorithms for Comparison
7.2 Generation of Problem Instances
7.3 Parameter Values
7.4 Comparison of 2BF-ILS with Other Metaheuristics
7.5 Comparison of 2BF-ILS with NEH
8 Conclusion
References
Route Planning for a Fleet of Electric Vehicles with Waiting Times at Charging Stations
1 Introduction
2 Related Literature
3 Mathematical Model
4 Genetic Algorithm (GA)
4.1 Constraint Programming (CP) for Fitness Evaluation
4.2 Selection and Crossover
4.3 Columns Based Chromosome Generation
4.4 Local Search on Solutions
4.5 Management of Charging Station Visits
5 Numerical Experiments
5.1 Results
6 Conclusions
References
Multiple Periods Vehicle Routing Problems: A Case Study
1 Introduction
2 Literature Review
3 Problem Description and Notation
3.1 Industrial Problem
3.2 Formal Description
4 Solution Approach
4.1 Weeks Planning Model
4.2 Days Planning Model
4.3 Routing Phase
5 Experimental Results
5.1 Instance Description
5.2 Weeks Planning Results
5.3 Days Planning Results
5.4 Routing Results
6 Conclusion
References
Rigorous Performance Analysis of State-of-the-Art TSP Heuristic Solvers
1 Introduction
2 Methodology
2.1 Instances
2.2 Solvers
2.3 Experimental Setup
2.4 Instance Features
2.5 Statistical Evaluation of Heuristic Performance
3 Performance Analysis
3.1 EAX v LKH+IPT Performance Results
3.2 LKH+GPX2 v EAX Performance Results
3.3 LKH+IPT v LKH+GPX2 Results
3.4 Overall Performance Classification
4 Heuristic Selection by Minimal Feature Extraction
5 Conclusions
References
Runtime Analysis of Discrete Particle Swarm Optimization Applied to Shortest Paths Computation
1 Introduction
2 Discrete PSO
3 Problem Structure
3.1 Shortest Path Trees
3.2 Objective Function
4 Model
5 Technical Results
6 Runtime Analysis
6.1 Upper Bounds
6.2 Lower Bounds
7 Conclusion
References
Quasi-Optimal Recombination Operator
1 Introduction
2 Background
2.1 Variable Interaction Graph
2.2 Recombination Graph
3 Dynastic Potential Exploration
3.1 Chordal Graphs
3.2 Clique Tree
3.3 Limiting the Complexity
3.4 Theoretical Comparison with (A)PX
4 Experiments
4.1 DPX Statistics
4.2 Comparison with PX and APX for NKQ Landscapes
4.3 Comparison with PX and APX for MAX-SAT
5 Conclusions
References
Insights into the Feature Selection Problem Using Local Optima Networks
1 Introduction
2 Background and Related Work
2.1 The Feature Selection Problem
2.2 Local Optima Networks
3 Experimental Setting
3.1 Datasets
3.2 Fitness Function
3.3 Feature Selection Algorithms
3.4 Local Optima Network Generation and Visualisation
4 Results
5 Conclusion
References
Clarifying the Difference in Local Optima Network Sampling Algorithms
1 Introduction
2 Definitions
2.1 The `Network'Feature Set
2.2 The `Funnel'Feature Set
3 Experimental Setting
3.1 Benchmark Test Problem
3.2 The Sampling Algorithms
3.3 Heuristics
3.4 Predictive Model Setup
4 Results
4.1 Sampling Algorithm Comparison
4.2 Prediction of Heuristic Competence on the Problems
5 Conclusions and Thoughts
References
A Unifying View on Recombination Spaces and Abstract Convex Evolutionary Search
1 Introduction
2 Recombination in the Geometric Framework
2.1 Abstract Convexity
2.2 Abstract Convex Search
3 Recombination in Elementary Landscapes Theory
3.1 Crossover Random Walk
3.2 Bridge to the Geometric Framework
4 Main Results
4.1 Axiomatic Classification of Crossovers
4.2 Algebraic Abstract Convex Search
5 Summary and Future Work
References
Program Trace Optimization with Constructive Heuristics for Combinatorial Problems
1 Introduction
2 PTO and Related Work
2.1 The Program Trace: A Universal Solution Representation
2.2 Solvers and Search Operators
2.3 The Role of the Generator
2.4 Open Questions
3 GRASP in PTO
3.1 Examples of Genotype-Phenotype Correspondence
4 New Repair Method in PTO
5 Experiments and Results
5.1 Problems and Instances
5.2 Experimental Design
5.3 Results
5.4 Discussion
6 Conclusions and Future Work
References
Correction to: Evolutionary Computation in Combinatorial Optimization
Correction to: A. Liefooghe and L. Paquete (Eds.): Evolutionary Computation in Combinatorial Optimization, LNCS 11452, https://doi.org/10.1007/978-3-030-16711-0
Author Index

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