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Foreword; Foreword; Preface; Contents; Nomenclature; Chapter 1 Introduction: Towards Multimodal Optimization; 1.1 Optimization and the Black Box; 1.1.1 Objective Function and Global Optimum; 1.1.2 The Locality Principle; 1.1.3 Local Optimality; 1.1.4 Basins of Attraction; 1.1.5 Optimization Problem Properties; 1.1.6 Different Approaches; 1.2 Multimodal Optimization; 1.3 Evolutionary Multimodal Optimization; 1.3.1 Roots; 1.3.2 The Common Framework; 1.3.3 Evolution Strategies; 1.3.4 EA Techniques for Multimodal Problems; 1.4 Objectives of ThisWork; 1.5 Book Structure and Usage Guide
Chapter 2 Experimentation in Evolutionary Computation2.1 Preamble: Justification for a Methodology; 2.2 The Rise of New Experimentalism in Computer Science; 2.2.1 New Experimentalism and the Top Quark; 2.2.2 Assessing Algorithms; 2.2.3 And What About EC?; 2.2.4 Something Different: The Algorithm Engineering Approach?; 2.3 Deriving an Experimental Methodology from Sequential Parameter Optimization; 2.3.1 The Basic Methodological Framework; 2.3.1.1 Research Questions, Claims, and Hypotheses; 2.3.1.2 Hypothesis Testing; 2.3.1.3 The Setup: Design and Terminology; 2.3.1.4 Reporting Experiments
2.3.2 Tuning Methods2.3.2.1 Initial Designs; 2.4 Parameters, Adaptability, and Experimental Analysis; 2.4.1 Parameter Tuning or Parameter Control?; 2.4.2 Adaptability; 2.4.2.1 Tuning Potential; 2.4.2.2 Tuning Effort; 2.4.2.3 Generalization of Adaptability Properties; 2.4.2.4 Facing the Future; Chapter 3 Groundwork for Niching; 3.1 Niching and Speciation in Nature; 3.2 Niching Definitions in Evolutionary Computation; 3.3 Niching Versus Repeated Local Search; 3.3.1 A Simple Niching Model; 3.3.2 Computable Results; 3.3.3 Simulated Results: Equal Basin Sizes
3.3.3.1 Experiment 3.1: How Are t2 and t3 Affected by Different Accuracies for Basin Identification and Basin Recognition?Pre-experimental planning; Task; Setup; Results/Visualization; Observations; Discussion; 3.3.4 Simulated Results: Unequal Basin Sizes; 3.3.4.1 Experiment 3.2: How Are t2 and t3 Affected If Basin Sizes Are Not Equal?; Task; Setup; Results/Visualization; Observations; Discussion; 3.4 Conclusions; Chapter 4 Basin Identification by Means of Nearest-Better Clustering; 4.1 Objectives; 4.2 The Basic Nearest-Better Clustering Algorithm; 4.3 Method Choice
4.3.1 Distance Computation4.3.1.1 Base Metrics; 4.3.1.2 Variable Normalization; 4.3.1.3 Aggregation of Mixed Type Data; 4.3.2 Mean Value Detection; 4.3.3 Connected Components Identification; 4.4 Correction for Large Sample Sizes and Small Dimensions; 4.4.1 Nearest Neighbor Distances Under Complete Spatial Randomness; 4.4.2 Obtaining an Approximate Nearest Neighbor Distance Distribution Function; 4.4.2.1 Experiment 4.1: Determine Maximum to Average NND Approximation; Pre-experimental Planning; Task; Setup; Result; Observations; Discussion; Refined Setup; Result; Observations; Discussion
Chapter 2 Experimentation in Evolutionary Computation2.1 Preamble: Justification for a Methodology; 2.2 The Rise of New Experimentalism in Computer Science; 2.2.1 New Experimentalism and the Top Quark; 2.2.2 Assessing Algorithms; 2.2.3 And What About EC?; 2.2.4 Something Different: The Algorithm Engineering Approach?; 2.3 Deriving an Experimental Methodology from Sequential Parameter Optimization; 2.3.1 The Basic Methodological Framework; 2.3.1.1 Research Questions, Claims, and Hypotheses; 2.3.1.2 Hypothesis Testing; 2.3.1.3 The Setup: Design and Terminology; 2.3.1.4 Reporting Experiments
2.3.2 Tuning Methods2.3.2.1 Initial Designs; 2.4 Parameters, Adaptability, and Experimental Analysis; 2.4.1 Parameter Tuning or Parameter Control?; 2.4.2 Adaptability; 2.4.2.1 Tuning Potential; 2.4.2.2 Tuning Effort; 2.4.2.3 Generalization of Adaptability Properties; 2.4.2.4 Facing the Future; Chapter 3 Groundwork for Niching; 3.1 Niching and Speciation in Nature; 3.2 Niching Definitions in Evolutionary Computation; 3.3 Niching Versus Repeated Local Search; 3.3.1 A Simple Niching Model; 3.3.2 Computable Results; 3.3.3 Simulated Results: Equal Basin Sizes
3.3.3.1 Experiment 3.1: How Are t2 and t3 Affected by Different Accuracies for Basin Identification and Basin Recognition?Pre-experimental planning; Task; Setup; Results/Visualization; Observations; Discussion; 3.3.4 Simulated Results: Unequal Basin Sizes; 3.3.4.1 Experiment 3.2: How Are t2 and t3 Affected If Basin Sizes Are Not Equal?; Task; Setup; Results/Visualization; Observations; Discussion; 3.4 Conclusions; Chapter 4 Basin Identification by Means of Nearest-Better Clustering; 4.1 Objectives; 4.2 The Basic Nearest-Better Clustering Algorithm; 4.3 Method Choice
4.3.1 Distance Computation4.3.1.1 Base Metrics; 4.3.1.2 Variable Normalization; 4.3.1.3 Aggregation of Mixed Type Data; 4.3.2 Mean Value Detection; 4.3.3 Connected Components Identification; 4.4 Correction for Large Sample Sizes and Small Dimensions; 4.4.1 Nearest Neighbor Distances Under Complete Spatial Randomness; 4.4.2 Obtaining an Approximate Nearest Neighbor Distance Distribution Function; 4.4.2.1 Experiment 4.1: Determine Maximum to Average NND Approximation; Pre-experimental Planning; Task; Setup; Result; Observations; Discussion; Refined Setup; Result; Observations; Discussion