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Real-world engineering problems often require concurrent
optimization of several design objectives, which are conflicting in
cases. This type of optimization is generally called
multi-objective or multi-criterion optimization. The area of
research that applies evolutionary methodologies to multi-objective
optimization is of special and growing interest. It brings a viable
computational solution to many real-world problems. Generally,
multi-objective engineering problems do not have a straightforward
optimal design. These kinds of problems usually inspire several
solutions of equal efficiency, which achieve different trade-offs.
Decision makers' preferences are normally used to select the most
adequate design. Such preferences may be dictated before or after
the optimization takes place. They may also be introduced
interactively at different levels of the optimization process.
Multi-objective optimization methods can be subdivided into
classical and evolutionary. The classical methods usually aim at a
single solution while the evolutionary methods provide a whole set
of so-called Pareto-optimal solutions. Evolutionary Multi-Objective
System Design: Theory and Applications provides a representation of
the state-of-the-art in evolutionary multi-objective optimization
research area and related new trends. It reports many innovative
designs yielded by the application of such optimization methods. It
also presents the application of multi-objective optimization to
the following problems: Embrittlement of stainless steel coated
electrodes Learning fuzzy rules from imbalanced datasets Combining
multi-objective evolutionary algorithms with collective
intelligence Fuzzy gain scheduling control Smart placement of
roadside units in vehicular networks Combining multi-objective
evolutionary algorithms with quasi-simplex local search Design of
robust substitution boxes Protein structure prediction problem Core
assignment for efficient network-on-chip-based system design
This book discusses a number of real-world applications of
computational intelligence approaches. Using various examples, it
demonstrates that computational intelligence has become a
consolidated methodology for automatically creating new competitive
solutions to complex real-world problems. It also presents a
concise and efficient synthesis of different systems using
computationally intelligent techniques.
This book discusses a number of real-world applications of
computational intelligence approaches. Using various examples, it
demonstrates that computational intelligence has become a
consolidated methodology for automatically creating new competitive
solutions to complex real-world problems. It also presents a
concise and efficient synthesis of different systems using
computationally intelligent techniques.
Real-world engineering problems often require concurrent
optimization of several design objectives, which are conflicting in
cases. This type of optimization is generally called
multi-objective or multi-criterion optimization. The area of
research that applies evolutionary methodologies to multi-objective
optimization is of special and growing interest. It brings a viable
computational solution to many real-world problems. Generally,
multi-objective engineering problems do not have a straightforward
optimal design. These kinds of problems usually inspire several
solutions of equal efficiency, which achieve different trade-offs.
Decision makers' preferences are normally used to select the most
adequate design. Such preferences may be dictated before or after
the optimization takes place. They may also be introduced
interactively at different levels of the optimization process.
Multi-objective optimization methods can be subdivided into
classical and evolutionary. The classical methods usually aim at a
single solution while the evolutionary methods provide a whole set
of so-called Pareto-optimal solutions. Evolutionary Multi-Objective
System Design: Theory and Applications provides a representation of
the state-of-the-art in evolutionary multi-objective optimization
research area and related new trends. It reports many innovative
designs yielded by the application of such optimization methods. It
also presents the application of multi-objective optimization to
the following problems: Embrittlement of stainless steel coated
electrodes Learning fuzzy rules from imbalanced datasets Combining
multi-objective evolutionary algorithms with collective
intelligence Fuzzy gain scheduling control Smart placement of
roadside units in vehicular networks Combining multi-objective
evolutionary algorithms with quasi-simplex local search Design of
robust substitution boxes Protein structure prediction problem Core
assignment for efficient network-on-chip-based system design
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