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Tinkle Chugh
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- affiliation: University of Exeter, UK
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2020 – today
- 2024
- [j8]Atanu Mazumdar
, Jana Burkotová
Handling simulation failures of a computationally expensive multiobjective optimization problem in pump design. Eng. Appl. Artif. Intell. 136: 108897 (2024) - [c21]Tinkle Chugh
Integrating Bayesian and Evolutionary Approaches for Multi-objective Optimisation. EvoApplications@EvoStar 2024: 391-406 - [c20]Frederico Leite Richardson
Is greed still good in multi-objective Bayesian optimisation? GECCO Companion 2024: 2103-2106 - 2023
- [j7]Atanu Mazumdar
Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems. Evol. Comput. 31(4): 375-399 (2023) - [c19]Arnaud Liefooghe
Feature-Based Benchmarking of Distance-Based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective. EMO 2023: 260-273 - [p3]Tinkle Chugh, António Gaspar-Cunha
Identifying Correlations in Understanding and Solving Many-Objective Optimisation Problems. Many-Criteria Optimization and Decision Analysis 2023: 241-267 - 2022
- [j6]Jonathan E. Fieldsend
A Visualizable Test Problem Generator for Many-Objective Optimization. IEEE Trans. Evol. Comput. 26(1): 1-11 (2022) - [j5]Atanu Mazumdar
Probabilistic Selection Approaches in Decomposition-Based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization. IEEE Trans. Evol. Comput. 26(5): 1182-1191 (2022) - [c18]Alexander Evans, Tinkle Chugh
Empirical Investigation of MOEAs for Multi-objective Design of Experiments. EA 2022: 145-158 - [c17]Tinkle Chugh, Endi Ymeraj:
Wind farm layout optimisation using set based multi-objective bayesian optimisation. GECCO Companion 2022: 695-698 - [c16]George De Ath
MBORE: multi-objective Bayesian optimisation by density-ratio estimation. GECCO 2022: 776-785 - [c15]Tinkle Chugh
R-MBO: a multi-surrogate approach for preference incorporation in multi-objective bayesian optimisation. GECCO Companion 2022: 1817-1825 - [c14]Tinkle Chugh
Mono-surrogate vs multi-surrogate in multi-objective bayesian optimisation. GECCO Companion 2022: 2143-2151 - [c13]Alma A. M. Rahat
Efficient Approximation of Expected Hypervolume Improvement Using Gauss-Hermite Quadrature. PPSN (1) 2022: 90-103 - [i7]George De Ath, Tinkle Chugh, Alma As-Aad Rahat:
MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation. CoRR abs/2203.16912 (2022) - [i6]Tinkle Chugh, Endi Ymeraj:
Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation. CoRR abs/2203.17065 (2022) - [i5]Tinkle Chugh:
R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation. CoRR abs/2204.13166 (2022) - [i4]Alma A. M. Rahat
Efficient Approximation of Expected Hypervolume Improvement using Gauss-Hermite Quadrature. CoRR abs/2206.07834 (2022) - [i3]Tinkle Chugh:
Mono-surrogate vs Multi-surrogate in Multi-objective Bayesian Optimisation. CoRR abs/2208.07240 (2022) - 2021
- [c12]Tinkle Chugh, Manuel López-Ibáñez:
Maximising hypervolume and minimising ϵ-indicators using Bayesian optimisation over sets. GECCO Companion 2021: 1326-1334 - [i2]Clym Stock-Williams, Tinkle Chugh, Alma A. M. Rahat, Wei Yu:
What Makes an Effective Scalarising Function for Multi-Objective Bayesian Optimisation? CoRR abs/2104.04790 (2021) - 2020
- [c11]Atanu Mazumdar
An Interactive Framework for Offline Data-Driven Multiobjective Optimization. BIOMA 2020: 97-109 - [c10]Tinkle Chugh:
Scalarizing Functions in Bayesian Multiobjective Optimization. CEC 2020: 1-8 - [p2]Tinkle Chugh
Towards Better Integration of Surrogate Models and Optimizers. High-Performance Simulation-Based Optimization 2020: 137-163 - [p1]Tinkle Chugh
Surrogate-Assisted Evolutionary Optimization of Large Problems. High-Performance Simulation-Based Optimization 2020: 165-187
2010 – 2019
- 2019
- [j4]Tinkle Chugh
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms. Soft Comput. 23(9): 3137-3166 (2019) - [j3]Yaochu Jin
Data-Driven Evolutionary Optimization: An Overview and Case Studies. IEEE Trans. Evol. Comput. 23(3): 442-458 (2019) - [j2]Ahsanul Habib
A Multiple Surrogate Assisted Decomposition-Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization. IEEE Trans. Evol. Comput. 23(6): 1000-1014 (2019) - [c9]Atanu Mazumdar
On Dealing with Uncertainties from Kriging Models in Offline Data-Driven Evolutionary Multiobjective Optimization. EMO 2019: 463-474 - [c8]Jonathan E. Fieldsend, Tinkle Chugh
A feature rich distance-based many-objective visualisable test problem generator. GECCO 2019: 541-549 - [c7]Tinkle Chugh
Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm. GECCO 2019: 1147-1155 - [c6]Tinkle Chugh, Alma A. M. Rahat
Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels. LOD 2019: 579-591 - [i1]Tinkle Chugh:
Scalarizing Functions in Bayesian Multiobjective Optimization. CoRR abs/1904.05760 (2019) - 2018
- [j1]Tinkle Chugh
A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. IEEE Trans. Evol. Comput. 22(1): 129-142 (2018) - [c5]Tinkle Chugh
Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies. GECCO 2018: 609-616 - 2017
- [c4]Tinkle Chugh
Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system. CEC 2017: 1541-1548 - 2016
- [c3]Tinkle Chugh
On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization. PPSN 2016: 214-224 - [c2]Jussi Hakanen, Tinkle Chugh
Connections of reference vectors and different types of preference information in interactive multiobjective evolutionary algorithms. SSCI 2016: 1-8 - 2015
- [c1]Tinkle Chugh
An Interactive Simple Indicator-Based Evolutionary Algorithm (I-SIBEA) for Multiobjective Optimization Problems. EMO (1) 2015: 277-291
Coauthor Index
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last updated on 2024-12-10 20:45 CET by the dblp team
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