Generalized Multitasking for Evolutionary Optimization of Expensive Problems
نویسندگان
چکیده
منابع مشابه
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...
متن کاملEvolutionary 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...
متن کاملMetamodeling Techniques For Evolutionary Optimization of Computationally Expensive Problems: Promises and Limitations
It is often the case in many problems in science and engineering that the analysis codes used are computationally very expensive. This can pose a serious impediment to the successful application of evolutionary optimization techniques. Metamodeling techniques present an enabling methodology for reducing the computational cost of such optimization problems. We present here a general framework fo...
متن کاملA Study on Evolutionary Multi-Objective Optimization with Fuzzy Approximation for Computational Expensive Problems
Recent progress in the development of Evolutionary Algorithms made them one of the most powerful and flexible optimization tools for dealing with Multi-Objective Optimization problems. Nowadays one challenge in applying MOEAs to real-world applications is that they usually need a large number of fitness evaluations before a satisfying result can be obtained. Several methods have been presented ...
متن کاملA multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems
Integrating data-driven surrogate models and simulation models of di erent accuracies (or delities) in a single algorithm to address computationally expensive global optimization problems has recently attracted considerable attention. However, handling discrepancies between simulation models with multiple delities in global optimization is a major challenge. To address it, the two major contrib...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Evolutionary Computation
سال: 2019
ISSN: 1089-778X,1089-778X,1941-0026
DOI: 10.1109/tevc.2017.2785351