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000737578 019__ $$a931008333$$a931593615
000737578 020__ $$a9783319074078$$qelectronic book
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000737578 1001_ $$aPreuss, Mike,$$eauthor.
000737578 24510 $$aMultimodal optimization by means of evolutionary algorithms$$h[electronic resource] /$$cMike Preuss.
000737578 264_1 $$aCham :$$bSpringer,$$c2015.
000737578 300__ $$a1 online resource.
000737578 336__ $$atext$$btxt$$2rdacontent
000737578 337__ $$acomputer$$bc$$2rdamedia
000737578 338__ $$aonline resource$$bcr$$2rdacarrier
000737578 4901_ $$aNatural computing series
000737578 504__ $$aIncludes bibliographical references and index.
000737578 5050_ $$aForeword; Foreword; Preface; Contents; Nomenclature; Chapter 1 Introduction: Towards Multimodal Optimization; 1.1 Optimization and the Black Box; 1.1.1 Objective Function and Global Optimum; 1.1.2 The Locality Principle; 1.1.3 Local Optimality; 1.1.4 Basins of Attraction; 1.1.5 Optimization Problem Properties; 1.1.6 Different Approaches; 1.2 Multimodal Optimization; 1.3 Evolutionary Multimodal Optimization; 1.3.1 Roots; 1.3.2 The Common Framework; 1.3.3 Evolution Strategies; 1.3.4 EA Techniques for Multimodal Problems; 1.4 Objectives of ThisWork; 1.5 Book Structure and Usage Guide
000737578 5058_ $$aChapter 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
000737578 5058_ $$a2.3.2 Tuning Methods2.3.2.1 Initial Designs; 2.4 Parameters, Adaptability, and Experimental Analysis; 2.4.1 Parameter Tuning or Parameter Control?; 2.4.2 Adaptability; 2.4.2.1 Tuning Potential; 2.4.2.2 Tuning Effort; 2.4.2.3 Generalization of Adaptability Properties; 2.4.2.4 Facing the Future; Chapter 3 Groundwork for Niching; 3.1 Niching and Speciation in Nature; 3.2 Niching Definitions in Evolutionary Computation; 3.3 Niching Versus Repeated Local Search; 3.3.1 A Simple Niching Model; 3.3.2 Computable Results; 3.3.3 Simulated Results: Equal Basin Sizes
000737578 5058_ $$a3.3.3.1 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
000737578 5058_ $$a4.3.1 Distance Computation4.3.1.1 Base Metrics; 4.3.1.2 Variable Normalization; 4.3.1.3 Aggregation of Mixed Type Data; 4.3.2 Mean Value Detection; 4.3.3 Connected Components Identification; 4.4 Correction for Large Sample Sizes and Small Dimensions; 4.4.1 Nearest Neighbor Distances Under Complete Spatial Randomness; 4.4.2 Obtaining an Approximate Nearest Neighbor Distance Distribution Function; 4.4.2.1 Experiment 4.1: Determine Maximum to Average NND Approximation; Pre-experimental Planning; Task; Setup; Result; Observations; Discussion; Refined Setup; Result; Observations; Discussion
000737578 506__ $$aAccess limited to authorized users.
000737578 520__ $$aThis book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary algorithms, and global optimization. The author explains niching in evolutionary algorithms and its benefits; he examines their suitability for use as diagnostic tools for experimental analysis, especially for detecting problem (type) properties; and he measures and compares the performances of niching and canonical EAs using different benchmark test problem sets. His work consolidates the recent successes in this domain, presenting and explaining use cases, algorithms, and performance measures, with a focus throughout on the goals of the optimization processes and a deep understanding of the algorithms used. The book will be useful for researchers and practitioners in the area of computational intelligence, particularly those engaged with heuristic search, multimodal optimization, evolutionary computing, and experimental analysis.
000737578 588__ $$aOnline resource; title from PDF title page (viewed December 10, 2015)
000737578 650_0 $$aEvolutionary programming (Computer science)
000737578 650_0 $$aEvolutionary computation.
000737578 77608 $$iPrint version:$$aPreuss, Mike$$tMultimodal Optimization by Means of Evolutionary Algorithms$$dCham : Springer International Publishing,c2015$$z9783319074061
000737578 830_0 $$aNatural computing series.
000737578 85280 $$bebk$$hSpringerLink
000737578 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-07407-8$$zOnline Access$$91397441.1
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000737578 980__ $$aEBOOK
000737578 980__ $$aBIB
000737578 982__ $$aEbook
000737578 983__ $$aOnline
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