Evolutionary Computation in Combinatorial Optimization
18th European Conference, EvoCOP 2018, Parma, Italy, April 4-6, 2018, Proceedings
Published on 23. March 2018
XIV, 189 pages
978-3-319-77449-7 (ISBN)
Description
This book constitutes the refereed proceedings of the 18th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2018, held in Parma, Italy, in April 2018, co-located with the Evo* 2018 events EuroGP, EvoMUSART and EvoApplications.
The 12 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers cover a wide spectrum of topics, ranging from the foundations of evolutionary computation algorithms and other search heuristics, to their accurate design and application to both single- and multi-objective combinatorial optimization problems. Fundamental and methodological aspects deal with runtime analysis, the structural properties of fitness landscapes, the study of metaheuristics core components, the clever design of their search principles, and their careful selection and configuration by means of automatic algorithm configuration and hyper-heuristics. Applicationscover conventional academic domains such as NK landscapes, binary quadratic programming, traveling salesman, vehicle routing, or scheduling problems, and also include real-world domains in clustering, commercial districting and winner determination.
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Arnaud Liefooghe | Manuel López-Ibáñez
Evolutionary Computation in Combinatorial Optimization
18th European Conference, EvoCOP 2018, Parma, Italy, April 4-6, 2018, Proceedings
Book
03/2018
Springer
€53.49
Shipment within 10-15 days
Content
- 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
- Author Index
