Parallel Problem Solving from Nature - PPSN XV

Name: Parallel Problem Solving from Nature - PPSN XV | 15th International Conference, Coimbra, Portugal, September 8-12, 2018, Proceedings, Part I
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15th International Conference, Coimbra, Portugal, September 8-12, 2018, Proceedings, Part I

Anne Auger Carlos M. Fonseca Nuno Lourenço Penousal Machado Luís Paquete Darrell Whitley(Editor)

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Published on 30. August 2018

XXX, 539 pages

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978-3-319-99253-2 (ISBN)

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Content

Intro
Preface
Organization
Invited Talks
The Shape of Art History in the Eyes of the Machine
Self-organization, Emergence and Stigmergy: Coordination from the Bottom-up
On Physarum Computations
Contents - Part I
Contents - Part II
Numerical Optimization
A Comparative Study of Large-Scale Variants of CMA-ES
1 Introduction
2 The bbob-Largescale COCO Testbed
3 The CMA-ES Algorithm and Some Large-Scale Variants
3.1 The (/w, )-CMA-ES
3.2 Large-Scale Variants of CMA-ES
4 Experimental Results
5 Discussion and Conclusion
References
Design of a Surrogate Model Assisted (1+1)-ES
1 Introduction
2 Related Work
3 Analysis
4 Step Size Adaptation and Experiments
5 Conclusions
References
Generalized Self-adapting Particle Swarm Optimization Algorithm
1 Introduction
2 Particle Swarm Optimization: Modification and Hybridization Approaches
3 Generalized Particle Swarm Optimization
4 Adaptation Scheme
5 Experiment Setup
6 Results
7 Conclusions and Future Work
References
PSO-Based Search Rules for Aerial Swarms Against Unexplored Vector Fields via Genetic Programming
1 Introduction
2 Background
3 Semantics for VFPS Evolution in EDDA
4 Experimental Study
5 Evolved VFPS
6 Analysis and Discussion of the Evolved VFPS
7 Conclusions and Future Work
References
Towards an Adaptive CMA-ES Configurator
1 Introduction
2 Modular CMA-ES
3 Data Processing
3.1 Generation and Pre-processing of the Data
3.2 Constructing Optimal Adaptive Configurations
3.3 Discarding Partially Successful Configurations
4 Results
4.1 Maximally Adaptive
4.2 Single Split
4.3 Discussion
5 Conclusion and Future Work
References
Combinatorial Optimization
A Probabilistic Tree-Based Representation for Non-convex Minimum Cost Flow Problems
1 Introduction
2 Preliminaries
2.1 Priority-Based Representation
3 Proposed Method
3.1 Probabilistic Tree-Based Representation
3.2 Genetic Algorithm with PTbR
4 Experimental Studies
4.1 Parameter Settings
4.2 Results and Analysis
5 Conclusion
References
Comparative Study of Different Memetic Algorithm Configurations for the Cyclic Bandwidth Sum Problem
1 Introduction
2 Memetic Algorithms for the CBSP
2.1 Solution Encoding and Initialization
2.2 Selection
2.3 Crossover
2.4 Mutation
2.5 Inversion
2.6 Survival Strategy
2.7 Local Search
3 Experimental Results
4 Conclusions and Future Work
References
Efficient Recombination in the Lin-Kernighan-Helsgaun Traveling Salesman Heuristic
1 Introduction
2 LKH Algorithm
3 Partition Crossover
3.1 IPT
3.2 GPX2
4 Results
5 Conclusions
References
Escherization with a Distance Function Focusing on the Similarity of Local Structure
1 Introduction
2 Related Work
2.1 Isohedral Tilings
2.2 Koizumi and Sugiharas's Formulation and Its Extension
2.3 The Weighted Normal Distance
3 Proposed Method
3.1 The Proposed Similarity Measure
3.2 The Extended Koizumi and Sugihara's Formulation with the AD Distance
3.3 A Tabu Search Algorithm
4 Experimental Results
5 Conclusion
References
Evolutionary Search of Binary Orthogonal Arrays
1 Introduction
2 Basic Definitions
3 Specification of GA and GP
3.1 Solutions Encoding
3.2 Fitness Function
3.3 Variation Operators
4 Analysis of the Search Space
5 Experiments
5.1 Problem Instances
5.2 Evolutionary Algorithms Parameters
5.3 Results
6 Conclusions and Perspectives
References
Heavy-Tailed Mutation Operators in Single-Objective Combinatorial Optimization
1 Introduction
2 Algorithms and Setting
2.1 The (1+1) Evolutionary Algorithm and Mutation Rates
2.2 Non-uniform Mutation Rates
3 Artificial Landscapes
3.1 General Bounds and the OneMax Function
3.2 A Comparison with Static Uniform Mutations
4 An Application to the Minimum Vertex Cover Problem
5 Maximizing Submodular Functions
5.1 A General Upper-Bound
5.2 An Application to the Maximum Directed Cut Problem
5.3 Experiments on Large Real Graphs
6 Discussion
References
Heuristics in Permutation GOMEA for Solving the Permutation Flowshop Scheduling Problem
1 Introduction
2 Permutation GOMEA
2.1 Solution and Model Encoding
2.2 Model Building
2.3 Optimal Mixing
2.4 Population Sizing Scheme
3 Permutation Flowshop Scheduling Benchmark
3.1 Problem Instances
3.2 Comparing Results
4 Heuristics for the PFSP
4.1 Constructive Heuristics
4.2 Constructive Heuristics Seeding: Results
4.3 Improvement Heuristics
5 Permutation GOMEA vs. VNS4 Iterated Local Search
6 Conclusions
References
On the Performance of Baseline Evolutionary Algorithms on the Dynamic Knapsack Problem
1 Introduction
2 Preliminaries
2.1 Problem Definition
2.2 Algorithms
3 Benchmarking for the Dynamic Knapsack Problem
3.1 The Dynamic Knapsack Problem
3.2 Benchmark and Experimental Setting
4 Experimental Results
4.1 Dynamic Uniform Constraint
4.2 Dynamic Linear Constraint
5 Conclusions and Future Work
References
On the Synthesis of Perturbative Heuristics for Multiple Combinatorial Optimisation Domains
1 Introduction
2 Background
3 Method
3.1 Grammatical Evolution
3.2 Grammar and Mechanics of the Operator
3.3 Problem Domains and Training Examples
4 Experiments
5 Results and Analysis
6 Conclusions
References
Genetic Programming
EDDA-V2 - An Improvement of the Evolutionary Demes Despeciation Algorithm
1 Introduction
2 Geometric Semantic Genetic Programming
3 Evolutionary Demes Despeciation Algorithm
4 Experimental Study
4.1 Test Problems
4.2 Experimental Settings
5 Results
6 Conclusions
References
Extending Program Synthesis Grammars for Grammar-Guided Genetic Programming
1 Introduction
2 Related Work
2.1 Grammar-Guided Genetic Programming
3 General Program Synthesis Benchmark Suite Remarks
4 Extending Program Synthesis Grammars
4.1 Data Type Char
4.2 Recursion
4.3 List Operations
4.4 Additional Methods
5 Experimental Setup
6 Results
6.1 Successful Solutions
6.2 Char Analysis
6.3 Recursion Analysis
7 Conclusion and Future Work
References
Filtering Outliers in One Step with Genetic Programming
1 Introduction
2 Background
2.1 Outliers
2.2 Robust Regression
3 Outlier Removal with Genetic Programming
3.1 Proposed Algorithm
3.2 Discussion
3.3 Related Works in GP
4 Experimental Evaluation
4.1 Experimental Setup
4.2 Results
5 Conclusions and Future Work
References
GOMGE: Gene-Pool Optimal Mixing on Grammatical Evolution
1 Introduction
2 Related Works
3 Grammatical Evolution
3.1 GOMGE: Gene-Pool Optimal Mixing EA for GE
4 Experimental Evaluation
5 Concluding Remarks
References
Self-adaptive Crossover in Genetic Programming: The Case of the Tartarus Problem
1 Introduction
2 Parameter Modification Approaches
3 The Tartarus Problem
3.1 Improved State Evaluation
4 Self-adaptive Crossover Operator
5 Conclusion
References
Multi-objective Optimization
A Decomposition-Based Evolutionary Algorithm for Multi-modal Multi-objective Optimization
1 Introduction
2 Proposed MOEA/D-AD
3 Experimental Settings
4 Experimental Results
4.1 Performance Comparison
4.2 Analysis of MOEA/D-AD
5 Conclusion
References
A Double-Niched Evolutionary Algorithm and Its Behavior on Polygon-Based Problems
1 Introduction
2 Related Works
2.1 Multi-modal Multi-objective Optimization Problems
2.2 Diversity Maintenance in the Objective and Decision Spaces
3 A Double-Niched Evolutionary Algorithm
4 Experiments
4.1 Polygon-Based Problems
4.2 Competing Algorithms and Parameter Settings
4.3 Results and Discussions
5 Conclusions
References
Artificial Decision Maker Driven by PSO: An Approach for Testing Reference Point Based Interactive Methods
1 Introduction
2 Background
3 Artificial Decision Maker Driven by PSO
4 Experimental Results
5 Conclusions and Future Work
References
A Simple Indicator Based Evolutionary Algorithm for Set-Based Minmax Robustness
1 Introduction and Background
2 Preliminaries
3 The SIBEA-R Method
4 Numerical Results
5 Conclusions
References
Extending the Speed-Constrained Multi-objective PSO (SMPSO) with Reference Point Based Preference Articulation
1 Introduction
2 Background
3 Algorithm Proposal
4 Experimental Setup
5 Results and Discussion
6 Use Case
7 Conclusions and Future Research Lines
References
Improving 1by1EA to Handle Various Shapes of Pareto Fronts
1 Introduction
2 Preliminaries
2.1 A Brief Introduction to 1by1EA
2.2 Motivation
3 1by1EA-II
4 Experiments and Discussions
5 Conclusions
References
New Initialisation Techniques for Multi-objective Local Search
1 Introduction
2 Background
2.1 Scalarisation-Based Local Search (SBLS)
2.2 Bi-objective Permutation Flowshop Scheduling
3 Archive-Aware SBLS Strategies
4 New SBLS Strategies: ChangeRestart, ChangeDirection
4.1 ChangeRestart
4.2 ChangeDirection
5 Experimental Setup
6 Experimental Results
6.1 Known SBLS Strategies vs. Their Archive-Aware Variants
6.2 Performance of the Two New SBLS Strategies
6.3 Analysis of Parameters Nscalar and Nsteps
7 Conclusion
References
Towards a More General Many-objective Evolutionary Optimizer
1 Introduction
2 Previous Related Work
3 Our Proposed Approach
3.1 Fast IGD+ Contribution
3.2 IGD+-MaOEA
4 Experimental Results
4.1 Parameters Settings
4.2 Comparison with MaOEAs Based on Convex Weight Vectors
4.3 Comparison with SMS-EMOA
5 Conclusions and Future Work
References
Towards Large-Scale Multiobjective Optimisation with a Hybrid Algorithm for Non-dominated Sorting
1 Introduction
1.1 Non-dominated Sorting: Definition and Algorithms
1.2 Our Motivation and Contribution
2 Preliminaries: The Algorithms to Hybridise
2.1 The Divide-and-Conquer Algorithm
2.2 The ENS-NDT Algorithm
3 The Proposed Algorithms
3.1 Loss of Monotonicity in HelperB
3.2 The ENS-NDT-ONE Algorithm
3.3 The Hybrid Algorithm
4 Experiments and Discussion
5 Conclusion
References
Tree-Structured Decomposition and Adaptation in MOEA/D
1 Introduction
2 The Proposed Method
2.1 Algorithm Framework
2.2 Domain Decomposition
2.3 Domain Adaptation
3 Subdomain Measurement
4 External Population
5 Comparison Study
6 Conclusions
References
Use of Reference Point Sets in a Decomposition-Based Multi-Objective Evolutionary Algorithm
1 Introduction
2 Basic Concepts
3 Our Proposed Approach
3.1 General Framework
3.2 Archiving Process
3.3 Reference Set
4 Experimental Results
4.1 Methodology
4.2 Parameterization
4.3 Discussion of Results
5 Conclusions and Future Work
References
Use of Two Reference Points in Hypervolume-Based Evolutionary Multiobjective Optimization Algorithms
Abstract
1 Introduction
2 Empirical Discussions on Reference Point Specification
3 Proposed Idea and Its Simple Implementation
4 Experimental Results by the Proposed Idea
5 Conclusions
Acknowledgments
References
Parallel and Distributed Frameworks
Introducing an Event-Based Architecture for Concurrent and Distributed Evolutionary Algorithms
1 Introduction
2 State of the Art
3 Event-Based Architectures and Implementing Evolutionary Algorithms over Them
4 Experiments and Results
5 Conclusions
References
Analyzing Resilience to Computational Glitches in Island-Based Evolutionary Algorithms
1 Introduction
2 Methodology
3 Experimentation
4 Conclusions
References
Spark Clustering Computing Platform Based Parallel Particle Swarm Optimizers for Computationally Expensive Global Optimization
Abstract
1 Introduction
2 Review
3 Spark-Based Parallel PSOs
3.1 Comparing Spark with Other Parallel Computing Technologies
3.2 Amdahl's Law for the Master-Slave Model
3.3 Spark-Based PEAs Framework for the Master-Slave Model
4 Numerical Experiments
4.1 The Spark Clustering Computing Platform
4.2 Analyses of Continuous Benchmark Functions
4.3 Comparisons on Computationally Expensive Functions
4.4 Comparisons on Functions with Linear Time Complexity
5 Conclusions and Future Research Directions
Acknowledgements
References
Weaving of Metaheuristics with Cooperative Parallelism
1 Introduction
2 The PHYSH Framework
3 PHYSH10: A Prototype Implementation
4 Experimental Evaluation
4.1 Evaluation of PHYSH-QAP on QAPLIB
4.2 Evaluation of PHYSH-QAP on Harder Instances
5 Conclusion and Future Directions
References
Applications
Conditional Preference Learning for Personalized and Context-Aware Journey Planning
1 Introduction
2 Background on CP-Net
3 Multimodal Journey Planning Tool
3.1 Journey Plan Attributes
3.2 Contextual Attributes
4 Algorithms' Evaluation
4.1 Experimental Setup
4.2 Result Analysis
5 Conclusions
References
Critical Fractile Optimization Method Using Truncated Halton Sequence with Application to SAW Filter Design
1 Introduction
2 Background and Problem Formulation
3 Approximation of CDF
3.1 Empirical CDF (ECDF)
3.2 Weighted Empirical CDF (W_ECDF)
3.3 Truncated Halton Sequence (THS)
4 Critical Fractile Optimization Method
4.1 Differential Evolution with Sample Saving Technique
4.2 Verification of Solution Using Monte Carlo Simulation
5 Numerical Experiment on Test Problem
5.1 Test Problem of CCP
5.2 Comparison Between JADEP and JADE
6 Application to SAW Filter Design
6.1 Structure and Mechanism of SAW Filer
6.2 Design of SAW Filer Under Uncertainty
6.3 Result of Experiment and Discussion
7 Conclusion
References
Directed Locomotion for Modular Robots with Evolvable Morphologies
1 Introduction
2 Related Work
3 Experimental Set-Up
3.1 Robots
3.2 Controllers
3.3 Experimental Parameters
4 Fitness Function
5 Experimental Results
6 Concluding Remarks
References
Optimisation and Illumination of a Real-World Workforce Scheduling and Routing Application (WSRP) via Map-Elites
1 Introduction
2 Previous Work
3 Methodology
3.1 The MAP-Elites Algorithm
3.2 The Evolutionary Algorithm
3.3 Problem Instances
3.4 Experimental Parameters
4 Results
4.1 Coverage and Precision
4.2 Gaining Insight into the Problem Domain
5 Conclusions
References
Prototype Discovery Using Quality-Diversity
1 Introduction
2 Related Work
2.1 Quality-Diversity and Surrogate Assistance
2.2 Dimensionality Reduction
3 Prototype Discovery Using Quality-Diversity
4 Evaluation
5 Conclusion
References
Sparse Incomplete LU-Decomposition for Wave Farm Designs Under Realistic Conditions
1 Introduction
2 Preliminaries
2.1 Objectives
2.2 Problem Complexity
3 Computational Speed-Ups and Constraint Handling
4 Experimental Study
5 Discussion
6 Conclusions
References
Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis
1 Introduction
2 Related Work
3 Modeling Framework
3.1 Symbolic Regression
3.2 Regression Trees
3.3 GP-Tree
4 Data and Computation
5 Results Analysis
6 Conclusion
References
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Parallel Problem Solving from Nature - PPSN XV

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