Evolutionary Computation in Combinatorial Optimization

Name: Evolutionary Computation in Combinatorial Optimization | 18th European Conference, EvoCOP 2018, Parma, Italy, April 4-6, 2018, Proceedings
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18th European Conference, EvoCOP 2018, Parma, Italy, April 4-6, 2018, Proceedings

Arnaud Liefooghe Manuel López-Ibáñez(Herausgeber*in)

Springer (Verlag)

Erschienen am 23. März 2018

XIV, 189 Seiten

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978-3-319-77449-7 (ISBN)

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Inhalt

Intro
Preface
Organization
Contents
Better Runtime Guarantees via Stochastic Domination
1 Introduction
2 Stochastic Domination
3 Domination-Based Fitness Level Method
4 Beyond the Fitness Level Theorem
5 Structural Domination
6 Conclusion
References
On the Fractal Nature of Local Optima Networks
1 Introduction
2 Background
2.1 The Study of Fitness Landscapes
2.2 The Local Optima Network
2.3 The Fractal Dimension
2.4 Fractals and Fitness Landscapes
2.5 Fractals and Complex Networks
3 Experimental Setting
3.1 Test Problem
3.2 Metaheuristics
3.3 Fractal Analysis
4 Results
4.1 Fractals and Epistasis
4.2 Fractal Dimension and Search Performance
5 Discussion
5.1 The Fractal Shape of Local Optima Networks
5.2 Connections with Search Difficulty
6 Conclusions and Future Work
References
How Perturbation Strength Shapes the Global Structure of TSP Fitness Landscapes
1 Introduction
2 Definitions and Algorithms
3 Empirical Methodology
3.1 Instances
3.2 Sampling Method
3.3 Performance and Network Metrics
4 Results and Analysis
4.1 Visualisation
4.2 Performance and Network Metrics Results
4.3 Impact of Perturbation Strength on Success Rate
4.4 Correlation Analysis
4.5 Correlation Variance Between Instance Classes
5 Conclusions
References
Worst Improvement Based Iterated Local Search
1 Introduction
2 Definitions
2.1 Fitness Landscapes and Related Concepts
2.2 Bit-String Landscapes Instances
3 Worst Improvement Hill-Climbing
3.1 Pivoting Rules
3.2 Additional Experiments
4 Experimental Analysis
4.1 Experimental Protocol
4.2 Results
4.3 ILS Performance and Landscape Features
5 Conclusion
References
Automatic Grammar-Based Design of Heuristic Algorithms for Unconstrained Binary Quadratic Programming
1 Introduction
2 Related Work
3 Proposed Approach
3.1 Grammar and the Heuristic Search Space
3.2 Automatic Design Using irace
4 Experiments and Results
4.1 Tuning with a Single Instance Set
4.2 Tuning with a Random Instance Set
5 Conclusions
References
Automatic Algorithm Configuration for the Permutation Flow Shop Scheduling Problem Minimizing Total Completion Time
1 Introduction
2 Automatic Algorithm Configuration
2.1 Grammar and Components
2.2 Solution Representation
2.3 Search Strategy
3 Computational Experiments
3.1 Benchmarks
3.2 Experimental Setup
3.3 Results
4 Conclusions
References
Data Clustering Using Grouping Hyper-heuristics
1 Introduction
2 Related Work
2.1 Solution Representation in Grouping Problems
2.2 Data Clustering
3 A Grouping Hyper-heuristic Approach to Solve Grouping Problems
3.1 Low Level Heuristics
3.2 Selection Hyper-heuristic Components
4 Application of Grouping Hyper-heuristics to Data Clustering
4.1 Experimental Data
4.2 Trials and Parameters Settings and CPU Specifications
4.3 Evaluation Criteria
4.4 Experimental Results and Remarks
5 Conclusion
References
Reference Point Adaption Method -1for Genetic Programming Hyper-Heuristic in Many-Objective Job Shop Scheduling
1 Introduction
2 Background
2.1 Problem Description of JSS
2.2 Related Work
3 Adaptive Reference Points for Many-Objective JSS
3.1 Fitness Evaluation
3.2 Reference Point Generation
4 Experiment Design
4.1 Parameter Settings
4.2 Data Set
4.3 Performance Measures
5 Results and Discussions
5.1 Overall Result
6 Conclusion
References
MOEA/DEP: An Algebraic Decomposition-Based Evolutionary Algorithm for the Multiobjective Permutation Flowshop Scheduling Problem
1 Introduction and Related Work
2 Multiobjective Optimization and MOEA/D Framework
3 Algebraic Differential Evolution for Permutations
4 MOEA/DEP
4.1 Initialization
4.2 Offsprings Generation
4.3 Population Update
4.4 Crossover for Permutations
5 Experiments
5.1 Parameters Calibration
5.2 Comparison with MEDA/D-MK
6 Conclusion and Future Work
References
An Evolutionary Algorithm with Practitioner's-Knowledge-Based Operators for the Inventory Routing Problem
1 Introduction
2 Problem Definition
3 Evolutionary Approach
3.1 Search Space and Solution Encoding
3.2 Initial Population
3.3 Recombination Operator
3.4 Date-Changing Mutation (DM)
3.5 Order-Changing Mutation (OM)
4 Experiments
5 Conclusions
References
A Multistart Alternating Tabu Search for Commercial Districting
1 Introduction
2 Definitions
3 Proposed Methods
3.1 Solution Construction
3.2 Optimizing Balance
3.3 Optimizing Compactness
3.4 Data Structures for Efficient Operations
4 Computational Experiments
4.1 Test Instances
4.2 Experimental Setup
4.3 Experiment 1: Constructive Algorithm
4.4 Experiment 2: Search Strategies
4.5 Experiment 3: Comparison with Existing Methods
5 Concluding Remarks
References
An Ant Colony Approach for the Winner Determination Problem
1 Introduction
1.1 Main Contributions
2 Winner Determination Problem
3 Literature Review
3.1 Exact Methods
3.2 Inexact Methods
4 Proposed Approach
4.1 Preprocessing Phase
4.2 Theoretical Convergence to Optimal for WDP
5 Randomized Pheromone Updating
5.1 Min-Max Pheromone Level
6 Randomized Graph Pruning
7 Experimental Results
8 Conclusion and Future Research
References
Erratum to: On the Fractal Nature of Local Optima Networks
Erratum to: Chapter "On the Fractal Nature of Local Optima Networks" in: A. Liefooghe and M. López-Ibáñez (Eds.): Evolutionary Computation in Combinatorial Optimization, LNCS 10782, https://doi.org/10.1007/978-3-319-77449-7_2
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