Evolutionary optimization in uncertain environments-a survey

By Yaochu Jin, J. Branke. IEEE Transactions on Evolutionary Computation, 2005

1,670 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/e07c31fa372fe94caba467bf25fe1e0e299aefc1

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Citations in the corpus

A survey on metaheuristics for stochastic combinatorial optimization (2009, background): “In the following we explain the working principles of these strategies, but for a detailed analysis and complete references, see (Jin and Branke 2005).”
Surrogate-assisted evolutionary computation: Recent advances and future challenges (2011, methodology): “If an expensive optimization is time-varying, evolutionary algorithms for solving dynamic optimization problems must be adopted to track the moving optima or moving Pareto front [97].”
Leveraging multiple simulators for crossing the reality gap (2012, methodology): “A robust fitness function [10] is also not employed, as the variation provided by the differences in how the dynamic simulation system evaluates the robots locomotion is hypothesized to provide enough variance to generate a robust control system.”
Recent advances in selection hyper-heuristics (2020, background): “The majority of solution techniques for dynamic environment problems are evolutionary approaches (Jin and Branke, 2005).”
Scalable Global Optimization via Local Bayesian Optimization (2019, neutral): “[22] for an overview in stochastic settings.”
On Using Surrogates with Genetic Programming (2015, methodology): “The data used to learn the surrogate model can come from an experimental design conducted before the EA, but usually is learned and adopted online during the run (Jin, 2005, 2011; Jin and Branke, 2005).”
Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels (2006, background): “For a general survey on the treatment of uncertainties in evolutionary algorithms (EA), the reader should refer to [ 1 ].”
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, methodology): “Initially addressed with tools from mathematical programming, robust optimization has also been studied with metaheuristics, with diﬀerent approximations to account for uncertainty during the search [130, 131].”
A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, background): “The field of dynamic optimization is closely related with EAs, known as evolutionary dynamic optimization (EDO) [12].”
A Multi-Facet Survey on Memetic Computation (2011, background): “There are potentially many other rich memeinspired designs, operational models, and algorithm frameworks which form the cornerstones of memetic computation as tools for problem-solving [3], [18], [23], [47]–[51].”
A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization (2014, methodology): “Using evolutionary algorithms to solve dynamic optimization problems has attracted increasing attention in recent years [20], [23], [24].”
Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System (2016, background): “It is very likely that the data are ill-distributed, imbalanced [57], incomplete [58], [59], and contaminated by noise [60], [61].”
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach (2006, background): “The performance of EAs in such dynamic environments has been studied mostly during the last two decades ([4], [5], [15], [23], [7], [10], [11] and [19]), and a plethora of performanceenhancement techniques has emerged.”
A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of Combustion (2009, neutral): “A broad literature overview of uncertainties addressed in evolutionary optimization is given in [33].”
Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation (2012, background): “Jin and Branke, 2005 ), which possibly leads to bias, wide confidence bands, and also error in the obtained optimum.”
Multiswarms, exclusion, and anti-convergence in dynamic environments (2006, background): “For an overview, the reader is referred to [12]–[14], [25], and [32].”