Adaptive Differential Evolution [electronic resource] : A Robust Approach to Multimodal Problem Optimization / by Jingqiao Zhang, Arthur C. Sanderson.

By: Zhang, Jingqiao [author.]Contributor(s): Sanderson, Arthur C [author.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Adaptation Learning and Optimization: 1Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009Description: XIII, 164 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642015274Subject(s): Engineering | Artificial intelligence | Mathematics | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics) | Operations Research/Decision Theory | Applications of MathematicsAdditional physical formats: Printed edition:: No titleDDC classification: 519 LOC classification: TA329-348TA640-643Online resources: Click here to access online
Contents:
Related Work and Background -- Theoretical Analysis of Differential Evolution -- Parameter Adaptive Differential Evolution -- Surrogate Model-Based Differential Evolution -- Adaptive Multi-objective Differential Evolution -- Application to Winner Determination Problems in Combinatorial Auctions -- Application to Flight Planning in Air Traffic Control Systems -- Application to the TPM Optimization in Credit Decision Making -- Conclusions and Future Work.
In: Springer eBooksSummary: Optimization problems are ubiquitous in academic research and real-world applications wherever such resources as space, time and cost are limited. Researchers and practitioners need to solve problems fundamental to their daily work which, however, may show a variety of challenging characteristics such as discontinuity, nonlinearity, nonconvexity, and multimodality. It is expected that solving a complex optimization problem itself should easy to use, reliable and efficient to achieve satisfactory solutions. Differential evolution is a recent branch of evolutionary algorithms that is capable of addressing a wide set of complex optimization problems in a relatively uniform and conceptually simple manner. For better performance, the control parameters of differential evolution need to be set appropriately as they have different effects on evolutionary search behaviours for various problems or at different optimization stages of a single problem. The fundamental theme of the book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. Topics covered in this book include: Theoretical analysis of differential evolution and its control parameters Algorithmic design and comparative analysis of parameter adaptive schemes Scalability analysis of adaptive differential evolution Adaptive differential evolution for multi-objective optimization Incorporation of surrogate model for computationally expensive optimization Application to winner determination in combinatorial auctions of E-Commerce Application to flight route planning in Air Traffic Management Application to transition probability matrix optimization in credit-decision making
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Related Work and Background -- Theoretical Analysis of Differential Evolution -- Parameter Adaptive Differential Evolution -- Surrogate Model-Based Differential Evolution -- Adaptive Multi-objective Differential Evolution -- Application to Winner Determination Problems in Combinatorial Auctions -- Application to Flight Planning in Air Traffic Control Systems -- Application to the TPM Optimization in Credit Decision Making -- Conclusions and Future Work.

Optimization problems are ubiquitous in academic research and real-world applications wherever such resources as space, time and cost are limited. Researchers and practitioners need to solve problems fundamental to their daily work which, however, may show a variety of challenging characteristics such as discontinuity, nonlinearity, nonconvexity, and multimodality. It is expected that solving a complex optimization problem itself should easy to use, reliable and efficient to achieve satisfactory solutions. Differential evolution is a recent branch of evolutionary algorithms that is capable of addressing a wide set of complex optimization problems in a relatively uniform and conceptually simple manner. For better performance, the control parameters of differential evolution need to be set appropriately as they have different effects on evolutionary search behaviours for various problems or at different optimization stages of a single problem. The fundamental theme of the book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. Topics covered in this book include: Theoretical analysis of differential evolution and its control parameters Algorithmic design and comparative analysis of parameter adaptive schemes Scalability analysis of adaptive differential evolution Adaptive differential evolution for multi-objective optimization Incorporation of surrogate model for computationally expensive optimization Application to winner determination in combinatorial auctions of E-Commerce Application to flight route planning in Air Traffic Management Application to transition probability matrix optimization in credit-decision making

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