Showing items with similar title or keywords.

• Handling expensive multiobjective optimization problems with evolutionary algorithms 

  Chugh, Tinkle (University of Jyväskylä, 2017)

  Multiobjective optimization problems (MOPs) with a large number of conflicting objectives are often encountered in industry. Moreover, these problem typically involve expensive evaluations (e.g. time consuming simulations ...

• Algorithmic issues in computational intelligence optimization : from design to implementation, from implementation to design 

  Caraffini, Fabio (University of Jyväskylä, 2016)

  The vertiginous technological growth of the last decades has generated a variety of powerful and complex systems. By embedding within modern hardware devices sophisticated software, they allow the solution of complicated ...

• Evolutionary Algorithms and Metaheuristics : Applications in Engineering Design and Optimization 

  Greiner, David; Periaux, Jacques; Quagliarella, Domenico; Magalhaes-Mendes, Jorge; Galván, Blas (Hindawi Publishing Corporation, 2018)

• Data-Driven Evolutionary Optimization : An Overview and Case Studies 

  Jin, Yaochu; Wang, Handing; Chugh, Tinkle; Guo, Dan; Miettinen, Kaisa (Institute of Electrical and Electronics Engineers, 2019)

  Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may ...

• Surrogate-Assisted Evolutionary Optimization of Large Problems 

  Chugh, Tinkle; Sun, Chaoli; Wang, Handing; Jin, Yaochu (Springer, 2020)

  This chapter presents some recent advances in surrogate-assisted evolutionary optimization of large problems. By large problems, we mean either the number of decision variables is large, or the number of objectives is ...