Surrogate-assisted evolutionary computation: Recent advances and future challenges
Y Jin - Swarm and Evolutionary Computation, 2011 - Elsevier
Surrogate-assisted, or meta-model based evolutionary computation uses efficient
computational models, often known as surrogates or meta-models, for approximating the …
computational models, often known as surrogates or meta-models, for approximating the …
A review of surrogate assisted multiobjective evolutionary algorithms
A Díaz-Manríquez, G Toscano… - Computational …, 2016 - Wiley Online Library
Multiobjective evolutionary algorithms have incorporated surrogate models in order to
reduce the number of required evaluations to approximate the Pareto front of …
reduce the number of required evaluations to approximate the Pareto front of …
Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection
T Akhtar, CA Shoemaker - Journal of Global Optimization, 2016 - Springer
GOMORS is a parallel response surface-assisted evolutionary algorithm approach to multi-
objective optimization that is designed to obtain good non-dominated solutions to black box …
objective optimization that is designed to obtain good non-dominated solutions to black box …
MOEA/D assisted by RBF networks for expensive multi-objective optimization problems
S Zapotecas Martínez, CA Coello Coello - Proceedings of the 15th …, 2013 - dl.acm.org
The development of multi-objective evolutionary algorithms assisted by surrogate models
has increased in the last few years. However, in real-world applications, the high modality …
has increased in the last few years. However, in real-world applications, the high modality …
Many-objective problems: challenges and methods
A López Jaimes, CA Coello Coello - Springer handbook of computational …, 2015 - Springer
This chapter presents a short review of the state-of-the-art efforts for understanding and
solving problems with a large number of objectives (usually known as many-objective …
solving problems with a large number of objectives (usually known as many-objective …
Surrogate-assisted multi-objective evolutionary optimization with Pareto front model-based local search method
Some local search methods have been incorporated into surrogate-assisted multi-objective
evolutionary algorithms to accelerate the search toward the real Pareto front (PF). In this …
evolutionary algorithms to accelerate the search toward the real Pareto front (PF). In this …
Efficient multi-objective optimization through population-based parallel surrogate search
T Akhtar, CA Shoemaker - arXiv preprint arXiv:1903.02167, 2019 - arxiv.org
Multi-Objective Optimization (MOO) is very difficult for expensive functions because most
current MOO methods rely on a large number of function evaluations to get an accurate …
current MOO methods rely on a large number of function evaluations to get an accurate …
An Analysis of the Operation Factors of Three PSO‐GA‐ED Meta‐Heuristic Search Methods for Solving a Single‐Objective Optimization Problem
In this study, we evaluate several nongradient (evolutionary) search strategies for
minimizing mathematical function expressions. We developed and tested the genetic …
minimizing mathematical function expressions. We developed and tested the genetic …
Integrating -dominance and RBF surrogate optimization for solving computationally expensive many-objective optimization problems
Multi-objective optimization of computationally expensive, multimodal problems is very
challenging, and is even more difficult for problems with many objectives (more than three) …
challenging, and is even more difficult for problems with many objectives (more than three) …
A direct local search mechanism for decomposition-based multi-objective evolutionary algorithms
SZ Martínez, CAC Coello - 2012 IEEE congress on evolutionary …, 2012 - ieeexplore.ieee.org
In recent years, the development of multi-objective evolutionary algorithms (MOEAs)
hybridized with mathematical programming techniques has significantly increased …
hybridized with mathematical programming techniques has significantly increased …