Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics - Info and Reading Options

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Intro -- Title Page -- Preface -- Organization -- Table of Contents -- The Importance of Being Careful -- Introduction -- The Tabu Tenure -- Varying Length TT -- Interaction of TT with Move Evaluation -- Localized Tabu Tenure -- Aspiration Criteria -- Why Not Ignore It? -- Searching in Infeasible Space -- When Feasibility Is Difficult to Obtain -- What Is the Best Move? -- Learning and Forgetting -- Other Tradeoff Issues -- Conclusions -- References -- Designing and Tuning SLS Through Animation and Graphics: An Extended Walk-Through -- Introduction -- The Visualization Tool: Viz -- A Step by Step Walk-Through with Ro-TS for QAP -- Experiment Set-Up: QAP Instances and Baseline Algorithm -- Fitness Landscapes of QAP and Ro-TS-I Behavior -- Hypotheses to Improve Walks on the QAP Fitness Landscapes -- Tweaking Ro-TS-I to Ro-TS-A for QAP (Type A) Instances -- Tweaking Ro-TS-I to Ro-TS-B for QAP (Type B) Instances -- Benchmarking on the Test Instances -- The Learning Process -- Conclusion -- References -- Implementation Effort and Performance -- Introduction -- Literature Overview -- Main Elements of the Analysis -- The Problem -- Metaheuristics -- The Tuning Process -- Experimental Analysis -- Conclusions -- References -- Tuning the Performance of the MMAS Heuristic -- Introduction and Motivation -- Background -- The MMAS Heuristic for the TSP -- Experiment Designs for Response Surface Models -- Methodology -- Stagnation Stopping Criterion -- Responses -- Problem Instances -- Experiment Design -- Model Fitting -- Model Verification -- Response Optimisation -- Optimisation Verification -- Related Work -- Contributions -- References -- Comparing Variants of MMAS ACO Algorithms on Pseudo-Boolean Functions -- Introduction -- Algorithms -- Plateau Functions -- Unimodal Functions, OneMax and LeadingOnes -- General Results -- OneMax -- LeadingOnes

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