By L. Shi, K. Rasheed (auth.), Yoel Tenne, Chi-Keong Goh (eds.)
In glossy technology and engineering, laboratory experiments are changed by way of excessive constancy and computationally dear simulations. utilizing such simulations reduces expenditures and shortens improvement instances yet introduces new demanding situations to layout optimization method. Examples of such demanding situations comprise restricted computational source for simulation runs, complex reaction floor of the simulation inputs-outputs, and etc.
Under such problems, classical optimization and research equipment may possibly practice poorly. This motivates the appliance of computational intelligence equipment akin to evolutionary algorithms, neural networks and fuzzy common sense, which frequently practice good in such settings. this is often the 1st publication to introduce the rising box of computational intelligence in dear optimization difficulties. subject matters lined include:
- Dedicated implementations of evolutionary algorithms, neural networks and fuzzy logic.
- Reduction of costly reviews (modelling, variable-fidelity, health inheritance).
- Frameworks for optimization (model administration, complexity keep an eye on, version selection).
- Parallelization of algorithms (implementation concerns on clusters, grids, parallel machines).
- Incorporation of specialist platforms and human-system interface.
- Single and multiobjective algorithms.
- Data mining and statistical analysis.
- Analysis of real-world circumstances (such as multidisciplinary layout optimization).
The edited booklet presents either theoretical remedies and real-world insights won via adventure, all contributed through best researchers within the respective fields. As such, it's a complete reference for researchers, practitioners, and advanced-level scholars attracted to either the idea and perform of utilizing computational intelligence for dear optimization problems.
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Additional info for Computational Intelligence in Expensive Optimization Problems
Shi and K. Rasheed can be generated by selecting the best individual from a number of uniformly distributed random individuals in the design space according to the approximate fitness [5, 43, 49]. Approximate fitness can also be used for crossover or mutation in a similar manner, through a technique known as Informed Operators [5, 17, 43, 49]. Under this approach, the approximate models are used to evaluate candidates only during the crossover and/or mutation process. After the crossover and/or mutation process, the exact fitness is still computed for the newly created candidate solutions.
LNCS, vol. 2723, pp. 610–621. : Neural Networks for Pattern Recognition. : Efficient evolutionary optimization using individualbased evolution control and neural networks: A comparative study. In: European Symposium on Artificial Neural Networks, pp. : Acceleration of the convergence speed of evolutionary algorithms using multi-layer neural networks. : Structure optimization of neural networks for aerodynamic optimization. : Neural networks for fitness approximation in evolutionary optimization.
The influence of migration sizes and intervals on island models. In: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. : Learning to be selective in genetic-algorithm-based design optimization. : Validating a model of colon colouration using an evolution strategy with adaptive approximations. , et al. ) GECCO 2004. LNCS, vol. 3103, pp. 1005–1016. : Decreasing the number of evaluations in evolutionary algorithms by using a meta-model of the fitness function. , Costa, E.
Computational Intelligence in Expensive Optimization Problems by L. Shi, K. Rasheed (auth.), Yoel Tenne, Chi-Keong Goh (eds.)