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[null,null,"Titolo",null,"Bioinspired optimization methods and their applications : 10th international conference, BIOMA 2022, Maribor, Slovenia, November 17-18, 2022 : proceedings / / Marjan Mernik, Tome Eftimov, and Matej Črepinsek (editors)"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-21094-8"]
[null,null,"Descrizione fisica",null,"1 online resource (288 pages)"]
[null,null,"Collana",null,"Lecture notes in computer science ; ; Volume 13627"]
[null,null,"Disciplina",null,"519.3"]
[null,null,"Soggetti",null,"Mathematical optimization"]
[{"$nulls":4},"Natural computation"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- An Agent-Based Model to Investigate Different Behaviours in a Crowd Simulation -- 1 Introduction -- 2 The Mathematical Model -- 3 NetLogo Model -- 4 Experimental Results -- 5 Conclusions and Future Works -- References -- Accelerating Evolutionary Neural Architecture Search for Remaining Useful Life Prediction -- 1 Introduction -- 2 Background -- 3 Method -- 3.1 Multi-objective Optimization -- 3.2 Speeding up Evaluation -- 4 Experimental Setup -- 4.1 Computational Setup and Benchmark Dataset -- 4.2 Data Preparation and Training Details -- 5 Results -- 6 Conclusions -- References -- ACOCaRS: Ant Colony Optimization Algorithm for Traveling Car Renter Problem -- 1 Introduction -- 2 Related Work -- 3 Problem Description -- 4 ACOCaRS Algorithm -- 5 Experiment -- 5.1 Testbed -- 5.2 Results -- 6 Discussion -- 7 Conclusion and Future Work -- References -- A New Type of Anomaly Detection Problem in Dynamic Graphs: An Ant Colony Optimization Approach -- 1 Introduction -- 2 Anomaly Detection Problem -- 3 Proposed Approach -- 4 Numerical Experiments -- 4.1 Benchmarks -- 4.2 Parameter Setting -- 4.3 Anomaly Detection in Real-World Networks -- 5 Conclusion and Further Work -- References -- .28em plus .1em minus .1emCSS-A Cheap-Surrogate-Based Selection Operator for Multi-objective Optimization -- 1 Introduction -- 2 Background -- 2.1 Spherical Search -- 2.2 Cheap Surrogate Selection "]
[{"$nulls":4},"(CSS) -- 3 Proposed Method -- 3.1 General Framework of CSS-MOEA -- 3.2 The Detailed Process of CSS-MOEA -- 4 Experiment Results -- 5 Conclusion -- References -- Empirical Similarity Measure for Metaheuristics -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 3.1 Metaheuristic Algorithms -- 3.2 Benchmark Functions -- 3.3 Parameter Tuning -- 4 Proposed Comparison Method -- 4.1 Algorithm Instances -- 4.2 Algorithm Profiling."]
[{"$nulls":4},"4.3 Measuring Similarity -- 5 Results -- 5.1 Comparing Instances of the Same Algorithm -- 5.2 Comparing Instances of the Same Tuning Function -- 5.3 Clustering the Algorithms' Instances Based on Similarity -- 5.4 Discussion -- 6 Conclusion -- References -- Evaluation of Parallel Hierarchical Differential Evolution for Min-Max Optimization Problems Using SciPy -- 1 Introduction -- 2 Definition of the Problem -- 3 Differential Evolution for MinMax Problems -- 3.1 Overview of Differential Evolution -- 3.2 Hierarchical (Nested) Differential Evolution and Parallel Model -- 4 Experimental Setup and Results -- 4.1 Benchmark Test Functions -- 4.2 Parameter Settings -- 4.3 Results and Discussion -- 5 Conclusion and Future Work -- References -- Explaining Differential Evolution Performance Through Problem Landscape Characteristics -- 1 Introduction -- 2 Related Work -- 3 Experimental Setup -- 3.1 Benchmark Problem Portfolio -- 3.2 Landscape Data -- 3.3 Algorithm Portfolio -- 3.4 Performance Data -- 3.5 Regression Models -- 3.6 Leave-One Instance Out Validation -- 3.7 SHAP Explanations -- 4 Results and Discussion -- 4.1 Optimization Algorithms Performance -- 4.2 Performance Prediction -- 4.3 Linking ELA Features to DE Performance -- 5 Conclusions -- References -- Genetic Improvement of TCP Congestion Avoidance -- 1 Introduction -- 2 Background -- 3 Related Works -- 4 Method -- 4.1 Code Simplification Procedure -- 5 Experimental Results -- 6 Conclusions and Future Work -- References -- Hybrid Acquisition Processes in Surrogate-Based Optimization. Application to Covid-19 Contact Reduction -- 1 Introduction -- 2 Background on Surrogate-Based Optimization -- 3 COVID-19 Contact Reduction Problem -- 4 Hybrid Acquisition Processes -- 5 Experiments -- 6 Conclusion -- References -- Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System."]
[{"$nulls":4},"1 Introduction -- 2 Related Work -- 3 The Supervised Rule-Based Learning System -- 4 Evaluation -- 4.1 Experiment Design -- 4.2 Results -- 5 Conclusion -- References -- Modified Football Game Algorithm for Multimodal Optimization of Test Task Scheduling Problems Using Normalized Factor Random Key Encoding Scheme -- 1 Introduction -- 2 Problem Description and Mathematical Modeling -- 3 The Proposed Modified Football Game Algorithm (mFGA) -- 3.1 Classic FGA -- 3.2 Modified FGA -- 4 Normalized Factor Random Key Encoding Scheme -- 5 Multimodal Single-Objective Optimization of TTSP -- 6 Comparison and Discussion -- 7 Conclusion and Future Works -- References -- Performance Analysis of Selected Evolutionary Algorithms on Different Benchmark Functions -- 1 Introduction -- 2 Related Work -- 3 Experiment -- 3.1 CEC 2022 Single Objective Bound Constrained Numerical Optimization -- 3.2 CEC 2021 Single Objective Bound Constrained Optimization -- 3.3 CEC 2017 Single Objective Bound Constrained Optimization -- 4 Discussion -- 5 Conclusion -- References -- Refining Mutation Variants in Cartesian Genetic Programming -- 1 Introduction -- 2 Related Work -- 3 Cartesian Genetic Programming -- 3.1 Introduction to Cartesian Genetic Programming -- 3.2 Mutation Algorithm -- 4 Further Changes in the Mutation Algorithm -- 4.1 Probabilistic Mutation -- 4.2 Single and Multiple Mutation -- 5 Preliminaries -- 5.1 Experiment Description -- "]
[{"$nulls":4},"5.2 Datasets -- 6 Experiments -- 6.1 Impact of Different Probabilistic Mutation Strategies -- 6.2 Impact of Multi-n and DMulti-n -- 7 Conclusion -- References -- Slime Mould Algorithm: An Experimental Study of Nature-Inspired Optimiser -- 1 Introduction -- 1.1 Slime Mould Algorithm -- 1.2 Previous Works -- 2 Newly Proposed Variants of SMA -- 2.1 Linear Reduction of the Population Size -- 2.2 Eigen Transformation -- 2.3 Perturbation."]
[{"$nulls":4},"2.4 Adaptation of Parameter z -- 3 Methods Used in Experiments -- 4 Experimental Settings -- 5 Results -- 6 Conclusion -- References -- SMOTE Inspired Extension for Differential Evolution -- 1 Introduction -- 2 Background -- 2.1 Differential Evolution -- 2.2 Synthetic Minority Oversampling Technique (SMOTE) -- 2.3 Literature Overview -- 3 Proposed Mechanism for Differential Evolution -- 4 Experimental Analysis -- 4.1 Setup -- 4.2 Comparison Against Other Mechanisms -- 4.3 Incorporation into Improved Algorithm Variants -- 5 Conclusion -- References -- The Influence of Local Search on Genetic Algorithms with Balanced Representations -- 1 Introduction -- 2 Background -- 2.1 Balanced Crossover Operators -- 2.2 Boolean Functions -- 3 Local Search of Boolean Functions -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Results -- 4.3 Discussion -- 5 Conclusions -- References -- Trade-Off of Networks on Weighted Space Analyzed via a Method Mimicking Human Walking Track Superposition -- 1 Introduction and Related Work -- 2 Simulation Model of WTSN on Weighted Space -- 2.1 Generation Process of WTSN on a Mixture of Different Ground Conditions -- 2.2 Pareto-Optimal Path Between Two Demand Vertices -- 2.3 Algorithm for WTSN on Weighted Space -- 3 Analysis of Differences in Pareto Frontier by Weighted Space -- 3.1 Experimental Spaces Setting -- 3.2 Result of Pareto Frontier Approximation -- 4 Discussion -- 5 Conclusion and Further Work -- References -- Towards Interpretable Policies in Multi-agent Reinforcement Learning Tasks -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Creation of the Teams -- 3.2 Fitness Evaluation -- 3.3 Individual Encoding -- 3.4 Operators -- 4 Experimental Setup -- 4.1 Environment -- 4.2 Parameters -- 5 Experimental Results -- 5.1 Interpretation -- 5.2 Comparison with a Non Co-Evolutionary Approach."]
[{"$nulls":4},"6 Conclusions and Future Works -- References -- Author Index."]