[Lecture Notes in Computer Science] Evolutionary Computation in Combinatorial Optimization Volume 12102 (20th European Conference, EvoCOP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings) ||

This book constitutes the refereed proceedings of the 20th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EuroGP, EvoMUSART and EvoApplications. The 14 full papers presented in this book 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 combinatorial optimization problems. Preface Organization Contents Optimizing Prices and Periods in Time-of-use Electricity Tariff Design Using Bilevel Programming 1 Introduction 2 Bilevel Modelling of Electricity Prices 2.1 Bilevel Model with Pre-defined Periods (M1) 2.2 Bilevel Model with Variable Periods (M2) 3 A Genetic Algorithm for the Variable Period Model 4 Results 5 Conclusions References An Algebraic Approach for the Search Space of Permutations with Repetition 1 Introduction 2 Algebraic Background 2.1 The Abstract Algebraic Framework for Evolutionary Computation 2.2 The Algebraic Differential Evolution 2.3 The Search Space of Permutations 3 Permutations with Repetition 3.1 Motivations and Preliminary Definitions 3.2 Discrete Operators for Permutations with Repetition 3.3 Implementation of the Discrete Operators 4 Algebraic Differential Evolution for Permutations with Repetition 5 ADE-PR for the Job Shop Scheduling Problem 5.1 Definition of the Problem 5.2 From a Permutation with Repetition to a JSSP Schedule 5.3 Local Search for the JSSP 5.4 Restart Scheme 6 Experiments 7 Conclusion and Future Work References A Comparison of Genetic Representations for Multi-objective Shortest Path Problems on Multigraphs 1 Introduction 2 Problem Description 3 Related Work 3.1 Genetic Representations for Paths in Simple Graphs 3.2 Genetic Representations for Paths in Multigraphs 3.3 Initialisation 4 New Representations and Initialisation 4.1 Extension of Representations 4.2 Heuristic Initialisation 5 Results 5.1 Implementation Details 5.2 Test Instances 5.3 Comparing Representations with Purely Random Initialisation 5.4 Comparing Initialisation Techniques 6 Conclusion and Future Work References The Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis 1 Introduction 2 Preliminaries 3 Run Time Analysis 4 Experiments 5 Conclusion References A Beam Search Approach to the Traveling Tournament Problem 1 Introduction 2 Previous Work 3 Problem Formalization 4 State Space Formulation 5 Beam Search 6 Lower Bounds Calculation 7 Computational Study 8 Conclusion and Future Work References Cooperative Parallel SAT Local Search with Path Relinking 1 Introduction 2 Local Search for SAT and MaxSAT 3 Parallel Local Search 4 Path Relinking 5 Experiments 5.1 SAT Experiments 5.2 Weighted Partial MaxSAT 6 Conclusions and Future Work References Dynamic Compartmental Models for Large Multi-objective Landscapes and Performance Estimation 1 Introduction 2 Methodology 2.1 Dynamic Compartmental Models for Multi-objective Evolutionary Algorithms 2.2 The NDNew-NDOld-DOM Feature Set 2.3 Performance Metrics and Features 3 Experimental Results 3.1 Test Problem and Experiment Settings 3.2 Fitting of the Models 4 Discussion 5 Conclusions and Future Work References Fitness Landscape Analysis of Automated Machine Learning Search Spaces 1 Introduction 2 Problem Definition 3 AutoML Fitness Landscape 3.1 Configurations 3.2 Neighborhood and Distance Between Pipelines 3.3 Fitness Function 4 AutoML Fitness Landscape Analysis 5 Experimental Analysis 5.1 Fitness Distance Correlation Analysis 5.2 Neutrality Analysis 6 Related Work 7 Conclusions and Future Work References On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D 1 Introduction 2 Background 2.1 Multi-objective Combinatorial Optimization 2.2 The Conventional MOEA/D Framework 3 Revising and Leveraging the Design of MOEA/D 3.1 Positioning and Rationale 3.2 The Proposed MOEA/D–(m, l, sps) Framework 3.3 Discussion and Outlook 4 Experimental Analysis 4.1 Experimental Setup 4.2 Impact of the Population Size: spsAll with Varying Values 4.3 Impact of the Sub-problem Selection Strategy 4.4 Robustness of MOEA/D–(, , spsRnd ) w.r.t. and 5 Conclusions and Perspectives References A Grouping Genetic Algorithm for Multi Depot Pickup and Delivery Problems with Time Windows and Heterogeneous Vehicle Fleets 1 Problem Identification 2 Related Work 3 A Two Index Formulation for the MDPDPTWHV 4 A Grouping Genetic Algorithm 4.1 Selection 4.2 Crossover Operator 4.3 Mutation Operator 4.4 Population Management 4.5 Repair Operator 5 Evaluation 5.1 Data Generation 5.2 Results 6 Discussion References MILPIBEA: Algorithm for Multi-objective Features Selection in (Evolving) Software Product Lines 1 Introduction 2 Background 2.1 Software Product Line Engineering 2.2 Multi-objective Optimisation 2.3 Evolution in SPL 2.4 SATIBEA 3 System Set-Up 3.1 Benchmark for Evolving FMs 3.2 Optimisation Objectives 3.3 Hypervolume Indicator 3.4 System and Algorithms Set-Up 4 Using Seeds in Evolving FM 5 Correcting Individuals 5.1 How SATIBEA Corrects Solutions 5.2 How Our MILP Technique Corrects Solutions 5.3 Comparison with Respect to the Correction Process 6 Performance of MILPIBEA vs. SATIBEA 6.1 On the Multi-objective Feature Selection Problem 6.2 With Evolved Feature Models 7 Conclusion and Future Work References A Group Genetic Algorithm for Resource Allocation in Container-Based Clouds 1 Introduction 2 Related Work and Background 2.1 Related Works 2.2 Group Genetic Algorithm (GGA) 3 Problem Model 4 The Proposed Group GA for the RAC Problem 4.1 Overall Framework 4.2 Representation 4.3 Initialization 4.4 Gene-Level Crossover 4.5 Rearrangement 4.6 Unpack 4.7 Merge 5 Experiment 5.1 Dataset and Test Instance 5.2 Benchmark Algorithms 5.3 Parameter Settings 5.4 Results 6 Conclusion and Future Work References The Local Optima Level in Chemotherapy Schedule Optimisation 1 Introduction 1.1 Background 1.2 Fitness Function 1.3 Evolutionary Search Algorithms 2 Methodology 2.1 Markov-Chain LON Construction Algorithm 2.2 Hybrid LON Construction Algorithm 3 Visualisations 4 Experimental Setup 4.1 Markov-Chain LON Construction: Details 4.2 Hybrid LON Construction: Details 4.3 Unknown Global Optimum 5 Results 5.1 The Hybrid LON 5.2 The Markov-Chain LON 5.3 A Study of Feasibility in LONs 5.4 Newly Proposed Optimisation Algorithms 6 Conclusions References Genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling 1 Introduction 2 Background 2.1 Dynamic Flexible Job Shop Scheduling 2.2 Genetic Programming Hyper-heuristic for DFJSS 3 The Proposed GP with Adaptive Search 4 Experiment Design 4.1 Simulation Model 4.2 Parameter Settings 4.3 Comparison Design 5 Results and Discussions 5.1 Performance of Evolved Rules 5.2 Unique Feature Analyses 5.3 The Frequency of Features 5.4 Reinitialised Individuals 5.5 Training Time 6 Conclusions and Future Work References Author Index