Metaheuristics for finding multiple solutions / / edited by Mike Preuss

• Record Nr.: UNISA996464434203316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-79553-5
• Descrizione fisica: 1 online resource (322 pages)
• Collana: Natural Computing Ser.
• Disciplina: 518.1
• Soggetti: Computational intelligence; Computer science; Artificial intelligence
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Foreword -- Preface -- Contents -- Multimodal Optimization: Formulation, Heuristics, and a Decade of Advances -- 1 Introduction -- 2 Definitions -- 2.1 The General Optimisation Problem -- 2.2 The Multimodal Optimization Problem -- 3 Performance Measures -- 4 Benchmark Suites and Problem Generators -- 5 Popular Algorithmic Approaches and History of the Field -- 6 Niching Competition Result Analysis -- 7 Conclusion -- References -- Representation, Resolution, and Visualization in Multimodal Optimization -- 1 Multimodal Optimization: The What and the Why -- 1.1 What Is a Mode? -- 1.2 Why Optimize Multiple Modes? -- 2 Representation, Resolution, and Basic Visualizations Plots -- 3 Visualizing the Multimodal Landscape: Local Optima Networks -- 4 Conclusion -- References -- Finding Representative Solutions in Multimodal Optimization for Enhanced Decision-Making -- 1 Introduction -- 2 Related Work -- 2.1 Classic Niching Methods -- 2.2 Recent Development -- 2.3 Differential Evolution -- 2.4 Hopkins-Statistic -- 2.5 Adaptable Non-maximal Suppression -- 3 Suppression-Radius-Based Niching (SRN) -- 3.1 Phase I-Identifying Representative Areas -- 3.2 Phase II-Guided Search Toward Representative Areas -- 4 Experiments -- 4.1 Experimental Design -- 4.2 Incorporating a User Specified Number of Optima -- 4.3 Automatic Estimation of the Number of Optima -- 4.4 No Specification ; of Number of Representatives -- 5 Conclusions -- References -- Lifting the Multimodality-Fog in Continuous Multi-objective Optimization -- 1 Introduction -- 2 Related Work -- 3 Multimodality in MO Optimization -- 3.1 Theoretical Foundations -- 3.2 Visualizing Landscapes of Multi-objective Gradients -- 4 On the Properties of State-of-the-Art Benchmarks -- 4.1 A Visual Overview -- 4.2 Interpretation and Categorization.; 5 How Multi-objective Optimization Algorithms Can Capitalize from Basins of Attraction -- 6 Conclusion -- References -- Towards Basin Identification Methods with Robustness Against Outliers -- 1 Introduction -- 2 Nearest-Better Clustering -- 3 Related Research -- 4 Ideas for New Basin Identification Methods -- 5 Experiments -- 5.1 Determining Regression Models -- 5.2 Validation -- 6 Conclusions -- References -- Deflection and Stretching Techniques for Detection of Multiple Minimizers in Multimodal Optimization Problems -- 1 Introduction -- 2 Deflection Technique -- 2.1 Basic Scheme -- 2.2 Variants and Applications -- 3 Stretching Technique -- 3.1 Basic Scheme -- 3.2 Variants and Applications -- 4 Experimental Evaluation -- 5 Conclusions -- References -- Multimodal Optimization by Evolution Strategies with Repelling Subpopulations -- 1 Introduction -- 2 Niching with Repelling Subpopulations -- 2.1 Core Algorithm -- 2.2 Main Niching Ideas -- 2.3 Evolution of Subpopulations -- 2.4 Restart Strategy with Increasing Population -- 2.5 Adaptation of the Normalized Taboo Distance -- 2.6 Boosting Time Efficiency -- 2.7 Initialization of Subpopulations -- 2.8 Parameter Setting -- 3 Numerical Evaluation -- 4 Summary and Conclusions -- References -- Two-Phase Real-Valued Multimodal Optimization with the Hill-Valley Evolutionary Algorithm -- 1 Introduction -- 2 Framework for Two-Phase MMO EAs -- 2.1 Initial Population Sampling -- 3 Fitness-Informed Clustering -- 3.1 Nearest-Better Clustering -- 3.2 Hierarchical Gaussian Mixture Learning -- 3.3 Hill-Valley Clustering -- 4 Core Search Algorithms -- 4.1 Termination Criteria for Core Search Algorithms -- 5 Experiments -- 5.1 Experiment 1: Clustering Comparison -- 5.2 Experiment 2: Core Search Algorithms and Clustering Methods -- 5.3 Experiment 3: MMO EA Comparison -- 5.4 Experiment 4: Larger Budget -- 6 Conclusion.; References -- Probabilistic Multimodal Optimization -- 1 Introduction -- 2 Probability Distribution-Based Niching -- 2.1 Existing Niching Methods -- 2.2 Locality Sensitive Hashing (LSH) -- 2.3 Fast Niching -- 2.4 Extensive Experiments -- 3 Probability Distribution-Based Optimization -- 3.1 Estimation of Distribution Algorithms (EDAs) -- 3.2 Ant Colony Optimization (ACO) -- 3.3 Multimodal Estimation of Distribution Algorithms (MEDAs) -- 3.4 Adaptive Multimodal Ant Colony Optimization (AM-ACO) -- 3.5 Extensive Comparison -- 4 Applications -- 5 Discussion and Future Work -- 6 Conclusion -- References -- Reduced Models of Gene Regulatory Networks: Visualising Multi-modal Landscapes -- 1 Introduction -- 2 Data-Driven Application: Gene Regulatory Network Models -- 2.1 Introduction to Gene Regulatory Networks and Circadian Rhythms -- 2.2 Boolean Delay Equations -- 2.3 An Exemplar Computational Model of Circadian Rhythms Based on BDEs -- 2.4 Parameter Optimisation of the BDE Model -- 3 Landscape Analysis -- 4 Local Optima Networks -- 5 Discussion -- References -- Grammar-Based Multi-objective Genetic Programming with Token Competition and Its Applications in Financial Fraud Detection -- 1 Introduction -- 2 Background -- 2.1 Multi-objective Optimization Problems -- 2.2 Genetic Programming (GP) -- 2.3 Financial Fraud Detection -- 3 Approach -- 3.1 Grammar-Based Multi-objective Genetic Programming (GBMGP) with Token Competition ; -- 3.2 Statistical Selection Learning -- 4 Experiments and Results -- 4.1 Introduction to Experiment Preparation -- 4.2 Parameter Settings -- 4.3 Results and Analysis -- 5 Conclusion -- 5.1 Contributions -- 5.2 Directions for Future Research -- References -- Phenotypic Niching Using Quality Diversity Algorithms -- 1 Introduction -- 2 The Search for Diversity -- 2.1 Genetic Diversity -- 2.2 Phenotypic Diversity -- 3 Quality Diversity.; 3.1 First Algorithms -- 3.2 General Description -- 3.3 A Practical Example -- 3.4 Success Stories -- 4 Insights -- 4.1 Alignment of Quality and Diversity -- 4.2 Stepping Stones -- 4.3 Alignment of Genome and Phenotype -- 4.4 Exploitation and Exploration -- 5 Comparing Performance -- 5.1 Performance Metrics -- 5.2 Benchmarks -- 6 Conclusions and Open Challenges -- References.