A Study on Metamodeling Techniques, Ensembles, and Multi-Surrogates in Surrogate-Assisted Memetic Algorithms
نویسندگان
چکیده
Surrogate-Assisted Memetic Algorithm(SAMA) is a hybrid evolutionary algorithm, particularly a memetic algorithm that employs surrogate models in the optimization search. Since most of the objective function evaluations in SAMA are approximated, the search performance of SAMA is likely to be affected by the characteristics of the models used. In this paper, we study the search performance of using different metamodeling techniques, ensembles, and multisurrogates in SAMA. In particular, we consider the SAMATRF, a SAMA model management framework that incorporates a trust region scheme for interleaving use of exact objective function with computationally cheap local metamodels during local searches. Four different metamodels, namely Gaussian Process (GP), Radial Basis Function (RBF), Polynomial Regression (PR), and Extreme Learning Machine (ELM) neural network are used in the study. Empirical results obtained show that while some metamodeling techniques perform best on particular benchmark problems, ensemble of metamodels and multi-surrogates yield robust and improved solution quality on the benchmark problems in general, for the same computational budget.
منابع مشابه
Memetic algorithm using multi-surrogates for computationally expensive optimization problems
In this paper, we present a Multi-Surrogates Assisted Memetic Algorithm (MSAMA) for solving optimization problems with computationally expensive fitness functions. The essential backbone of our framework is an evolutionary algorithm coupled with a local search solver that employs multi-surrogates in the spirit of Lamarckian learning. Inspired by the notion of 'blessing and curse of uncertainty'...
متن کاملSurrogate-assisted evolutionary computation: Recent advances and future challenges
Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted ...
متن کاملBuilding Ensembles of Surrogates by Optimal Convex Combination
When using machine learning techniques for learning a function approximation from given data it can be difficult to select the right modelling technique. Without preliminary knowledge about the function it might be beneficial if the algorithm could learn all models by itself and select the model that suits best to the problem, an approach known as automated model selection. We propose a general...
متن کاملEvolution by Adapting Surrogates
To deal with complex optimization problems plagued with computationally expensive fitness functions, the use of surrogates to replace the original functions within the evolutionary framework is becoming a common practice. However, the appropriate datacentric approximation methodology to use for the construction of surrogate model would depend largely on the nature of the problem of interest, wh...
متن کاملPnm-1: Attitudes to Surrogacy in Women Attending to The Selected Shahrekord Public Health Centers in 1392
Background: There has been little interest in the research literature on public opinions regarding assisted conception and surrogacy, particularly in Iran. Nevertheles, the growing evidence shows that problems in adaptation and coping may be related to perceived normative values. Materials and Methods: In this cross sectional study, questionnaires on attitudes to surrogacy were completed by 187...
متن کامل