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Publications

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| 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 |

2024

• Chugh T, Evans A. (2024) Integrating Bayesian and Evolutionary Approaches for Multi-objective Optimisation, Applications of Evolutionary Computation, Springer Nature Switzerland, 391-406, DOI:10.1007/978-3-031-56855-8_24. [PDF]

2023

• Bhut S, Kumar K, Chugh T, Sahlot P. (2023) Machine Learning Based Prediction of Energy Density for Additively Manufactured AlSi10Mg Samples by Laser Powder Bed Fusion Process, 2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pages 465-469, DOI:10.1109/ICCSAI59793.2023.10421132.
• Evans A, Chugh T. (2023) Empirical Investigation of MOEAs for Multi-objective Design of Experiments, Lecture Notes in Computer Science, Springer Nature Switzerland, 145-158, DOI:10.1007/978-3-031-42616-2_11. [PDF]
• Chugh T, Gaspar-Cunha A, Deutz AH, Duro JA, Oara DC, Rahat A. (2023) Identifying Correlations in Understanding and Solving Many-Objective Optimisation Problems, Many-Criteria Optimization and Decision Analysis, Springer Nature, 241-267, DOI:10.1007/978-3-031-25263-1_9.
• Mazumdar A, López-Ibáñez M, Chugh T, Hakanen J, Miettinen K. (2023) Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems, Evolutionary Computation, volume 31, no. 4, pages 375-399, DOI:10.1162/evco_a_00329. [PDF]
• Liefooghe A, Verel S, Chugh T, Fieldsend J, Allmendinger R, Miettinen K. (2023) Feature-Based Benchmarking of Distance-Based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective, Lecture Notes in Computer Science, Springer Nature Switzerland, 260-273, DOI:10.1007/978-3-031-27250-9_19. [PDF]

2022

• Chugh T. (2022) Mono-surrogate vs Multi-surrogate in Multi-objective Bayesian Optimisation. [PDF]
• Rahat A, Chugh T, Fieldsend J, Allmendinger R, Miettinen K. (2022) Efficient Approximation of Expected Hypervolume Improvement using Gauss-Hermite Quadrature, Parallel Problem Solving from Nature (PPSN), Dortmund, Germany, 10th - 14th Sep 2022.
• Saad S, Javadi AA, Chugh T, Farmani R. (2022) Optimal management of mixed hydraulic barriers in coastal aquifers using multi-objective Bayesian optimization, Journal of Hydrology, volume 612, pages 128021-128021, article no. 128021, DOI:10.1016/j.jhydrol.2022.128021. [PDF]
• Chugh T. (2022) R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation. [PDF]
• Chugh T. (2022) R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation, Genetic and Evolutionary Computation Conference (GECCO ’22), Boston, Ma, Usa, 9th - 13th Jul 2022, DOI:10.1145/3520304.3533973.
• Chugh T, Ymeraj E. (2022) Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation. [PDF]
• De Ath G, Chugh T, Rahat A. (2022) MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation, Genetic and Evolutionary Computation Conference (GECCO ’22), Boston, Ma, Usa, 9th - 13th Jul 2022, DOI:10.1145/3512290.3528769.
• Mazumdar A, Chugh T, Hakanen J, Miettinen K. (2022) Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization, IEEE Transactions on Evolutionary Computation, volume PP, no. 99, pages 1-1, DOI:10.1109/tevc.2022.3154231.
• Fieldsend JE, Chugh T, Allmendinger R, Miettinen K. (2022) A Visualizable Test Problem Generator for Many-Objective Optimization, IEEE Transactions on Evolutionary Computation, volume 26, no. 1, pages 1-11, DOI:10.1109/TEVC.2021.3084119.

2021

• Chugh T, Lopez-Ibanez M. (2021) Maximising Hypervolume and Minimising 𝜖-Indicators using Bayesian Optimisation over Sets, Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, DOI:10.1145/3449726.3463178. [PDF]
• Stock-Williams C, Chugh T, Rahat A, Yu W. (2021) What Makes an Effective Scalarising Function for Multi-Objective Bayesian Optimisation?. [PDF]

2020

• Chugh T. (2020) Scalarizing Functions in Bayesian Multiobjective Optimization, 2020 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC). [PDF]
• Mazumdar A, Chugh T, Hakanen J, Miettinen K. (2020) An Interactive Framework for Offline Data-Driven Multiobjective Optimization, Bioinspired Optimization Methods and Their Applications. BIOMA 2020, Bruxelles, Belgium, 19th - 20th Nov 2020, DOI:10.1007/978-3-030-63710-1_8.
• Palar PS, Zuhal LR, Chugh T, Rahat A. (2020) On the Impact of Covariance Functions in Multi-Objective Bayesian Optimization for Engineering Design, AIAA Scitech 2020 Forum, AIAA Scitech 2020 Forum, DOI:10.2514/6.2020-1867. [PDF]
• Chugh T, Rahat A, Volz V, Zaefferer M. (2020) Towards better integration of surrogate models and optimizers, Studies in Computational Intelligence, 137-163, DOI:10.1007/978-3-030-18764-4_7.
• Chugh T. (2020) Scalarizing Functions in Bayesian Multiobjective Optimization, 2020 IEEE Congress on Evolutionary Computation (CEC), DOI:10.1109/cec48606.2020.9185706. [PDF]

2019

• Mazumdar A, Chugh T, Miettinen K, López-Ibáñez M. (2019) On Dealing with Uncertainties from Kriging Models in Offline Data-Driven Evolutionary Multiobjective Optimization, Evolutionary Multi-Criterion Optimization, Springer Nature, 463-474, DOI:10.1007/978-3-030-12598-1_37.
• Chugh T. (2019) Scalarizing Functions in Bayesian Multiobjective Optimization, DOI:10.48550/arxiv.1904.05760.
• Chugh T, Rahat A, Palar PS. (2019) Trading-off Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels, The Fifth International Conference on Machine Learning, Optimization, and Data Science (LOD 2019), Siena - Tuscany, Italy, 10th - 13th Sep 2019.
• Kaur J, Chugh T, Sangal VK. (2019) Energy efficient global optimisation of reactive dividing wall distillation column, Indian Chemical Engineer, DOI:10.1080/00194506.2019.1623089.
• Chugh T, Kratky T, Miettinen K, Jin Y, Makkonen P. (2019) Multiobjective Shape Design in a Ventilation System with a Preference-driven Surrogate-assisted Evolutionary Algorithm, The Genetic and Evolutionary Computation Conference (GECCO ’19), Prague, Czech Republic, 13th - 17th Jul 2019, DOI:10.1145/3321707.3321745.
• Chugh T, Sun C, Wang H, Jin Y. (2019) Surrogate-Assisted Evolutionary Optimization of Large Problems, High-Performance Simulation-Based Optimization, Springer International Publishing, 165-187, DOI:10.1007/978-3-030-18764-4_8.
• Chugh T, Rahat A, Volz V, Zaefferer M. (2019) Towards Better Integration of Surrogate Models and Optimizers, High-Performance Simulation-Based Optimization, Springer International Publishing, 137-163, DOI:10.1007/978-3-030-18764-4.
• Fieldsend JE, Chugh T, Allmendinger R, Miettinen K. (2019) A Feature Rich Distance-Based Many-Objective Visualisable Test Problem Generator, Genetic and Evolutionary Computation Conference (GECCO ’19), Prague, 13th - 17th Jul 2019, DOI:10.1145/3321707.3321727.
• Majumdar A, Chugh T, Miettinen K, Lopez-Ibanez M. (2019) On Dealing with Uncertainties from Kriging Models in Offline Data-Driven Evolutionary Multiobjective Optimization, Evolutionary Multi-Criterion Optimization, East Lansing, Mi, Usa, 10th - 13th Mar 2019, DOI:10.1007/978-3-030-12598-1_37.
• A. H, Chugh T, Singh HK, Ray T, Miettinen K. (2019) A Multiple Surrogate Assisted Decomposition Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization, IEEE Transactions on Evolutionary Computation, DOI:10.1109/TEVC.2019.2899030. [PDF]
• Jin Y, Wang H, Chugh T, Guo D, Miettinen K. (2019) Data-Driven Evolutionary Optimization: An Overview and Case Studies, IEEE Transactions on Evolutionary Computation, volume 23, no. 3, pages 442-458, DOI:10.1109/TEVC.2018.2869001.
• Chugh T, Sindhya K, Hakanen J, Miettinen K. (2019) A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms, Soft Computing, volume 23, pages 3137-3166, DOI:10.1007/s00500-017-2965-0.

2018

• Chugh T. (2018) The code for the K-RVEA algorithm. [PDF]
• Chugh T, Allmendinger R, Ojalehto V, Miettinen K. (2018) Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies, The Genetic and Evolutionary Computation Conference (GECCO) 2018, Kyoto, Japan, 15th - 19th Jul 2018, DOI:10.1145/3205455.3205514. [PDF]

2017

• Hakanen J, Chugh T, Sindhya K, Jin Y, Miettinen K. (2017) Interactive K-RVEA: interactive evolutionary multiobjective optimization algorithm for computationally expensive problems, Multiple Criteria Decision Making (MCDM), Ottawa, Canada, 10th - 14th Jul 2017.
• Chugh T. (2017) Handling expensive multiobjective optimization problems with evolutionary algorithms. [PDF]
• Chugh T, Sindhya K, Miettinen K, Jin Y, Kratky T, Makkonen P. (2017) Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system, 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings, pages 1541-1548, DOI:10.1109/CEC.2017.7969486.
• Chugh T, Chakraborti N, Sindhya K, Jin Y. (2017) A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem, MATERIALS AND MANUFACTURING PROCESSES, volume 32, no. 10, pages 1172-1178, DOI:10.1080/10426914.2016.1269923. [PDF]

2016

• Sindhya K, Rauhala T, Chugh T, Jin Y, Miettinen K, Hakanen J. (2016) Multiobjective Optimization in Assessment of Transmission Network Compensation Strategy, 28th European Conference on Operational Research 2016, Poznan, Poland, 3rd - 7th Jul 2016.
• Chugh T, Yaochu J, Kaisa M, Jussi H, Karthik S. (2016) A Kriging-assisted evolutionary algorithm for many-objective optimization.
• Chugh T, Jin Y, Miettinen K, Hakanen J, Sindhya K. (2016) A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization, IEEE Transactions on Evolutionary Computation, volume 22, pages 129-142, DOI:10.1109/TEVC.2016.2622301. [PDF]
• Chugh T, Sindhya K, Miettinen K, Hakanen J, Jin Y. (2016) On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization, PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XIV, volume 9921, pages 214-224, DOI:10.1007/978-3-319-45823-6_20. [PDF]
• Hakanen J, Chugh T, Sindhya K, Jin Y, Miettinen K. (2016) Connections of Reference Vectors and Different Types of Preference Information in Interactive Multiobjective Evolutionary Algorithms, PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI). [PDF]

2015

• Chugh T, Sindhya K, Hakanen J, Miettinen K, Jin Y. (2015) A surrogate assisted inverse model based evolutionary multiobjective optimization algorithm for computationally expensive problems, Multiple Criteria Decision Making (2015), Hamburg, Germany, 2nd - 7th Aug 2015.
• Chugh T. (2015) An Interactive Simple Indicator-Based Evolutionary Algorithm (I-SIBEA) for Multiobjective Optimization Problems. [PDF]
• Chugh T, Sindhya K, Hakanen J, Miettinen K. (2015) Handling Computationally Expensive Multiobjective Optimization Problems with Evolutionary Algorithms: A Survey.
• Chugh T, Sindhya K, Hakanen J, Miettinen K. (2015) An Interactive Simple Indicator-Based Evolutionary Algorithm (I-SIBEA) for Multiobjective Optimization Problems, EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PT I, volume 9018, pages 277-291, DOI:10.1007/978-3-319-15934-8_19. [PDF]

2014

• Chugh T. (2014) Handling computationally expensive multi-objective optimization problems using evolutionary algorithms: A survey, n International conference for Mathematical Modeling and Optimization in Mechanics (MMOM) 2014, Jyvaskyla, Finland, 6th - 7th Mar 2014.
• Mogilicharla A, Chugh T, Majumdar S, Mitra K. (2014) Multi-Objective Optimization of Bulk Vinyl Acetate Polymerization with Branching, MATERIALS AND MANUFACTURING PROCESSES, volume 29, no. 2, pages 210-217, DOI:10.1080/10426914.2013.872271. [PDF]

2013

• M. A, Chugh T, Mitra K, Majumdar S. (2013) Effect of live radical species in controlled branching of bulk free radical polymerization system: A multi objective evolutionary approach, International Conference on Advances in Chemical Engineering (ACE 2013), Roorkee, India, 22nd - 24th Feb 2013.

2012

• Chugh T, M. A, Mitra K, Majumdar S. (2012) Optimal process conditions for the controlled branching of free radical polymerization: A case study, Chemcon 2012, Jalandhar, India, 27th - 30th Dec 2012.

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