Patent Filed: D. Aggarwal, D.K. Saxena, T. Bäck, M. Emmerich, Crew Optimization, Netherlands Patent Application N2025010, Feb. 2020
 Enhanced Innovized Progress Operator for Evolutionary Multi-and Many-objective Optimization, S. Mittal, D. K. Saxena, K. Deb and E. D. Goodman, IEEE Transactions on Evolutionary Computation, 2022, doi: 10.1109/TEVC.2021.3131952.
 A Learning-based Innovized Progress Operator for Faster Convergence in Evolutionary Multi-objective Optimization, S. Mittal, D.K. Saxena, K. Deb, and E.D. Goodman; ACM Transactions on Evolutionary Learning and Optimization, 2022, Volume 2, Issue 1, 1-29 (https://doi.org/10.1145/3474059)
 Online summarization of dynamic graphs using subjective interestingness for sequential data, S. Kapoor, D. K. Saxena, and M. van Leeuwen, Data Mining and Knowledge Discovery, 35, 88–126, 2021, https://doi.org/10.1007/s10618-020-00714-8
 Discovering Subjectively Interesting Multigraph Patterns, S. Kapoor, D.K. Saxena and M. van Leeuwen;Machine Learning, Vol 109, 2020: https://doi.org/
 A new replica placement strategy based on multi-objective optimisation for HDFS; Y. Li, M. Tian, Y. Wang, Q. Zhang, D. K. Saxena, and L. Jiao; International Journal of Bio-Inspired Computation, 16(1), 2020, 13-22
 On Timing the Nadir-Point Estimation and/or Termination of Reference-Based Multi- and Many-objective Evolutionary Algorithms; D. K. Saxena and Sarang Kapoor; Evolutionary Multi-Criterion Optimization, 191-202, 2019.
 Timing the Decision Support for Real-World Many-Objective Optimization Problems; J. A Duro, D. K. Saxena; Evolutionary Multi-Criterion Optimization, 191-205, 2017.
 Entropy based Termination Criterion for Multiobjective Evolutionary Optimisation; D. K. Saxena, Arnab Sinha, J. A. Duro and Q. Zhang; IEEE Transactions on Evolutionary Computation, 20 (4), 485-498, 2016 Code
 Machine learning based decision support for many-objective optimization problems; J.A.Duro, D. K.Saxena, K.Deb and Q.Zhang; Neurocomputing, Volume 146, Pages 30–47. http://www.sciencedirect.com/science/article/pii/S0925231214008753
 Objective Reduction in Many-objective Optimization: Linear and Nonlinear Algorithms; D. K.Saxena, J.A.Duro, A.Tiwari, K.Deb and Q.Zhang; IEEE Transactions on Evolutionary Computation, 2012, 99, 1-23. Code
 An Evolutionary Multi-objective Framework for Business Process Optimization; K.Vergidis, D.K.Saxena and A.Tiwari; Applied Soft Computing, 2012, 2638-2653.
 Identifying the Redundant and Ranking the Critical Constraints in Practical Optimization Problems; D.K.Saxena, A.Rubino, J.A.Duro and A.Tiwari; Engineering Optimization, 2012, 1-23.
 Using Objective Reduction and Interactive Procedure to Handle Many-objective optimization Problems; A.Sinha, D.K.Saxena, K.Deb and A.Tiwari, Applied Soft Computing, 2013, 3(1), 415-427.
 Framework for Many-objective Test Problems with both Simple and Complicated Pareto-set Shapes; D.K.Saxena, Q.Zhang, J.A.Duro and A.Tiwari; Evolutionary Multi-Criterion optimization, 2011, 197-211.
 On Handling a Large Number of Objectives A Posteriori and During Optimization; D.Brockhoff, D.K.Saxena, K.Deb and E.Zitzler; Multi-objective Problem Solving from Nature, 2008, 4, 377-403.
 Non-linear Dimensionality Reduction Procedures for certain Large-dimensional Multi-objective Optimization Problems: Employing Correntropy and a Novel Maximum Variance Unfolding; D.K.Saxena and K.Deb; Evolutionary Multi-Criterion Optimization, 2007, 772-787.
Refereed Conference Papers
 A Generic and Computationally Efficient Automated Innovization Method for Power-Law Design Rules; K. Garg, A. Mukherjee, S. Mittal, D. K. Saxena and K. Deb; Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion), July 8–12, 2020, Cancún, Mexico. ACM, New York, NY, USA: https://doi.org/10.1145/
 Learning based Multi-objective Optimization Through ANN-Assisted Online Innovization; S. Mittal, D. K. Saxena and K. Deb; In Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion), July 8–12, 2020, Cancún, Mexico. ACM, New York, NY, USA: https://doi.org/10.1145/
 A Unified Automated Innovization Framework Using Threshold-based Clustering; S. Mittal, D. K. Saxena and K. Deb; Proceedings of Congress on Evolutionary Computation (CEC-2020), Piscataway, NJ: IEEE Press.
 Service Information in the Provision of Support Service Solutions: A State-of-the-art Review; S. Kundu, A. McKay, R. Cuthbert, D. McFarlane, D. K. Saxena, A. Tiwari and P. Johnson; CIRP Industrial Product-Service Systems; Cranfield, U.K, 2009, ISBN: 978-0-9557436-5-8, 100-106.
 Constrained many-objective optimization: A way forward; D. K. Saxena, T. Ray, K. Deb and A. Tiwari; IEEE Congress on Evolutionary Computation, Trondheim, Norway, 2009, ISBN:978-1-4244-2958-5, 545-552.
 Dimensionality Reduction of Objectives and Constraints in multi-objective optimization problems: A system design perspective; D. K. Saxena and K. Deb; IEEE Congress on Evolutionary Computation, Hongkong, 2008, ISBN:978-1-4244-1822-0, 3204-3211.
 Trading on infeasibility by exploiting constraint’s criticality through multi-objectivization: A system design perspective; D. K. Saxena and K. Deb; IEEE Congress on Evolutionary Computation, Singapore, 2007, ISBN:978-1-4244-1339-3, 919-926.
 Searching for Pareto-optimal Solutions through Dimensionality Reduction for Certain Large-dimensional Multi-Objective Optimization Problems; K. Deb and D.K.Saxena; IEEE Congress on Evolutionary Computation, Vancouvar, Canada, 2006, IEEE: 0-7803-9487-9, 3353-3360.
Deliverables to "British Aerospace Systems & Engineering and Physical Sciences Research Council, UK"
for the project: "S4T : Support Service Solutions: Strategy and Transition"
|1||Current state of service information||2008||31||5||University of - Leeds, Cranfield, & Cambridge, UK.|
|2||Service information requirements||2009||43||6||University of - Cranfield, Cambridge, & Leeds, UK.|
|3||Blueprint for future service information||2009||37||5||University of - Leeds, Cranfield, & Cambridge, UK.|
Industrial case studies
|2009||30||5||University of - Cranfield, Cambridge, & Leeds, UK.|
|5||A roadmap for the transition to future service information solutions||009||11||10||University of - Cambridge, Leeds, Cranfield, & BAES, UK.|
 Aggarwal, D., Saxena, D.K., Bäck, T., Emmerich, M. (March, 2020). AirCROP: Airline Crew Pairing Optimizer for Complex Flight Networks Involving Multiple Crew Bases & Billion-Plus Variables. EADAL Report Number 2020001. [pdf] NEW
 Aggarwal, D., Saxena, D.K., Bäck, T., Emmerich, M. (March, 2020). On Initializing Airline Crew Pairing Optimization for Large-scale Complex Flight Networks. EADAL Report Number 2020002. [pdf] NEW
 Aggarwal, D., Saxena, D.K., Bäck, T., Emmerich, M. (July, 2019). Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method. EADAL Report Number 2019001. [pdf]