Designing supervised learning systems is in general a multi-objective optimization problem. It requires finding appropriate trade-offs between several objectives, for example between model complexity and accuracy or sensitivity and specificity. We consider the adaptation of kernel and regularization parameters of support vector machines (SVMs) by means of multi-objective evolutionary optimizati...

The structural design problems are acknowledged to be commonly multicriteria in nature. The various multi-criteria optimization methods are reviewed and most efficient and easy-to-use Pare to optimal solution method is applied to structural optimization of a sandwich beam.

1. Abstract In engineering problems the randomness and uncertainties are inherent, thus the scatter of structural parameters from their nominal ideal values is unavoidable. Robust Design Optimization (RDO) methods primarily seek to minimize the influence of stochastic variations on the mean design, and traditionally rely on rough approximations of the stochastic response about the mean design. ...

Simulation optimization is an approach that is used to solve problems involving stochastic components. This paper presents a Lexicographic Nelder–Mead (LNM) based simulation optimization (LNM-SO) method to solve multi-criteria simulation optimization problems. The method is designed to be relatively easy-to-implement and be applicable to a wide range of problem domains. To effectively evaluate ...

There has always been a need to solve real-life large-scale problems, such as efficiently allocating limited resources, and other complex and conflicting situations related to combinatorial optimization genre. A class of combinatorial optimization problems is NP-hard and, among many well-known, several of them are assignment, planning and rescheduling problems. Assignment problems can deal with...