Описание: Multi-objective optimization problems (MOPs) and uncertain optimization problems (UOPs) which widely exist in real life are challengeable problems in the fields of decision making, system designing, and scheduling, amongst others. Decomposition exploits the ideas of aEURO~making things simpleaEURO (TM) and aEURO~divide and conqueraEURO (TM) to transform a complex problem into a series of simple ones with the aim of reducing the computational complexity. In order to tackle the abovementioned two types of complicated optimization problems, this book introduces the decomposition strategy and conducts a systematic study to perfect the usage of decomposition in the field of multi-objective optimization, and extend the usage of decomposition in the field of uncertain optimization.
Описание: Multiple Threshold Spatially Uniform ReliefF for the Genetic Analysis of Complex Human Diseases.- Time-Point Specific Weighting Improves Coexpression Networks from Time-Course Experiments.- Inferring Human Phenotype Networks from Genome-Wide Genetic.- Knowledge-Constrained K-Medoids Clustering of Regulatory Rare Alleles for Burden Tests.- Feature Selection and Classification of High Dimensional Mass Spectrometry Data: A Genetic Programming Approach.- Structured Populations and the Maintenance of Sex.- Hybrid Multiobjective Artificial Bee Colony with Differential Evolution Applied to Motif Finding.- ACO-Based Bayesian Network Ensembles for the Hierarchical Classification of Ageing-Related Proteins.- Dimensionality Reduction via Isomap with Lock-Step and Elastic Measures for Time Series Gene Expression Classification.- Supervising Random Forest Using Attribute Interaction Networks.- Hybrid Genetic Algorithms for Stress Recognition in Optimal Use of Biological Expert Knowledge from Literature.- Mining in Ant Colony Optimization for Analysis of Epistasis in Human Disease.- A Multiobjective Proposal Based on the Firefly Algorithm for Inferring Phylogenies.- Mining for Variability in the Coagulation Pathway: A Systems Biology Approach.- Improving the Performance of CGPANN for Breast Cancer Diagnosis Using Crossover and Radial Basis Functions.- An Evolutionary Approach to Wetlands Design.- Impact of Different Recombination Methods in a Mutation-Specific MOEA for a Biochemical Application.- Cell-Based Metrics Improve the Detection of Gene-Gene Interactions Using Multifactor Dimensionality Reduction.- Emergence of Motifs in Model Gene Regulatory Networks.
Описание: Constitutes the refereed proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2009, held in Tubingen, Germany, in April 2009 co located with the Evo 2009 events. This book includes such topics as biomarker discovery, cell simulation and modeling, and ecological modeling.
Описание: Constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain in April 2007, colocated with the Evo 2007 events. This book presents 28 revised full papers that were reviewed and selected from 60 submissions.
Автор: Liefooghe Название: Evolutionary Computation in Combinatorial Optimization ISBN: 3319774484 ISBN-13(EAN): 9783319774480 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Transistor-level design for complex mixed-signal systems-on-chip remains difficult to automate. This book shows how a modified genetic algorithm kernel can improve efficiency in the analog IC design cycle and includes a worked example of the method.
Автор: Daniel Ashlock Название: Evolutionary Computation for Modeling and Optimization ISBN: 1441919694 ISBN-13(EAN): 9781441919694 Издательство: Springer Рейтинг: Цена: 10055.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets.
Lots of applications and test problems, including a biotechnology chapter.
More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers.
This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy.
Автор: Shengxiang Yang; Xin Yao Название: Evolutionary Computation for Dynamic Optimization Problems ISBN: 3642448437 ISBN-13(EAN): 9783642448430 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems.
Описание: This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics.
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