Evolutionary Computation in Combinatorial Optimization - Info and Reading Options

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Intro -- Preface -- Organization -- Contents -- A Computational Study of Neighborhood Operators for Job-Shop Scheduling Problems with Regular Objectives -- 1 Introduction -- 2 The Job-Shop Scheduling Problem -- 3 Neighborhood Structures for Job Shop Scheduling -- 4 Experimental Study -- 4.1 Experimental Evaluation -- 4.2 Iterative Best Improvement Versus Iterative First Improvement for Single Neighborhoods -- 4.3 First-Improvement Variable Neighborhood Descent -- 4.4 Iterated Local Search Algorithm -- 5 Conclusions -- References -- A Genetic Algorithm for Multi-component Optimization Problems: The Case of the Travelling Thief Problem -- 1 Introduction -- 2 Travelling Thief Problem -- 3 Multi-component Genetic Algorithm -- 4 Methodology and Results -- 5 Conclusion and Future Work -- References -- A Hybrid Feature Selection Algorithm Based on Large Neighborhood Search -- 1 Introduction -- 2 Literature Review -- 3 WFLNS: A Wrapper Filter Feature Selection Based on LNS -- 3.1 Encoding Representation and Initialization -- 3.2 Destroy and Repair Methods -- 3.3 Objective Function -- 3.4 Acceptance Method -- 4 Experimental Results and Discussion -- 4.1 Classification Accuracy -- 4.2 Effect of Destruction Degree Parameter -- 4.3 Effect of Acceptance Criteria -- 5 Conclusion -- References -- A Memetic Algorithm to Maximise the Employee Substitutability in Personnel Shift Scheduling -- 1 Introduction -- 2 Problem Definition and Formulation -- 3 A Memetic Algorithm to Maximise the Employee Substitutability -- 3.1 Population Initialisation -- 3.2 Local Search (LS) -- 3.3 Repair (R) -- 3.4 Evolutionary Cycle -- 4 Computational Experiments -- 4.1 Test Design -- 4.2 Validation of the Proposed Procedure -- 5 Conclusions -- References

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