A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms

Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling

We present a parallel evolutionary optimization algorithm that leverages surrogate models for solving computationally expensive design problems with general constraints, on a limited computational budget. The essential backbone of our framework is an evolutionary algorithm coupled with a feasible sequential quadratic programming solver in the spirit of Lamarckian learning.We employ a trust-regi...

Evolutionary Optimization for Computationally expensive problems using Gaussian Processes

The use of statistical models to approximate detailed analysis codes for evolutionary optimization has attracted some attention [1-3]. However, those early methodologies do suffer from some limitations, the most serious of which being the extra tuning parameter introduceds. Also the question of when to include more data points to the approximation model during the search remains unresolved. Tho...

Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms

In this talk, fitness assignment in multiobjective evolutionary algorithms is interpreted as a multi-criterion decision process. A suitable decision making framework based on goals and priorities is formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies, including constraint satisfaction, lexicographic optimization, and a form...

Handling Preferences in Evolutionary Multiobjective Optimization: A Survey

Despite the relatively high volume of research conducted on evolutionary multiobjec-tive optimization in the last few years, little attention has been paid to the decision making process that is required to select a nal solution to the multiobjective optimization problem at hand. This paper reviews the most important preference handling approaches used with evolutionary algorithms, analyzing th...

Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objective Evaluations