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Nelishia Pillay
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2020 – today
- 2024
- [j35]Kyle W. Pretorius, Nelishia Pillay:
Neural network crossover in genetic algorithms using genetic programming. Genet. Program. Evolvable Mach. 25(1): 7 (2024) - [j34]Rahul Kapoor, Nelishia Pillay:
A genetic programming approach to the automated design of CNN models for image classification and video shorts creation. Genet. Program. Evolvable Mach. 25(1): 10 (2024) - [j33]Jose M. Tapia-Avitia, Jorge M. Cruz-Duarte
, Iván Amaya, José Carlos Ortiz-Bayliss
Analysing hyper-heuristics based on Neural Networks for the automatic design of population-based metaheuristics in continuous optimisation problems. Swarm Evol. Comput. 89: 101616 (2024) - [c92]Mia Gerber, Nelishia Pillay:
Dynamic Algorithm Composition for Image Segmentation. CEC 2024: 1-8 - [c91]Mia Gerber, Nelishia Pillay:
Dynamic Function Generation for Text Classification. CEC 2024: 1-8 - [c90]Guillermo Pérez-Espinosa, Jorge M. Cruz-Duarte, Iván Amaya, José Carlos Ortiz-Bayliss, Hugo Terashima-Marín, Nelishia Pillay:
Tailoring Metaheuristics for Designing Thermodynamic-Optimal Cooling Devices for Microelectronic Thermal Management Applications. CEC 2024: 1-8 - [c89]Weiyao Meng
Machine Learning for Evolutionary Computation - the Vehicle Routing Problems Competition. GECCO Companion 2024: 13-14 - [c88]Nelishia Pillay
Transfer Learning in Evolutionary Spaces. GECCO Companion 2024: 1005-1016 - [c87]Mia Gerber
Dynamic Neural Architecture Search for Image Classification. GECCO Companion 2024: 1554-1562 - 2023
- [c86]Emilio Singh
A Study of Ant-Based Pheromone Spaces for Generation Perturbative Hyper-Heuristics. GECCO 2023: 84-92 - [c85]Mia Gerber
Automated algorithm composition of unsupervised image clustering algorithms. GECCO Companion 2023: 467-470 - [c84]Mia Gerber
An analysis of choice functions for Fuzzy ART using grammatical evolution. GECCO Companion 2023: 571-574 - [c83]Rahul Kapoor
Iterative Structure-Based Genetic Programming for Neural Architecture Search. GECCO Companion 2023: 595-598 - [c82]Jeffrey Russell
A Selection Hyper-Heuristic for Transfer Learning in Genetic Programming. GECCO Companion 2023: 631-634 - [c81]Nelishia Pillay
Transfer Learning in Evolutionary Spaces. GECCO Companion 2023: 1037-1047 - 2022
- [j32]Cry Kuranga
A comparative study of nonlinear regression and autoregressive techniques in hybrid with particle swarm optimization for time-series forecasting. Expert Syst. Appl. 190: 116163 (2022) - [j31]Jared O'Reilly
Supplementary-architecture weight-optimization neural networks. Neural Comput. Appl. 34(13): 11177-11197 (2022) - [j30]Emilio Singh
A study of ant-based pheromone spaces for generation constructive hyper-heuristics. Swarm Evol. Comput. 72: 101095 (2022) - [c80]Tapotosh Ghosh
A Privacy-Preserving Federated-MobileNet for Facial Expression Detection from Images. AII 2022: 277-292 - [c79]Jorge M. Cruz-Duarte, Iván Amaya, José Carlos Ortiz-Bayliss, Nelishia Pillay:
A Transfer Learning Hyper-heuristic Approach for Automatic Tailoring of Unfolded Population-based Metaheuristics. CEC 2022: 1-8 - [c78]Ahmed Hassan, Nelishia Pillay:
Automated Design of Hybrid Metaheuristics: A Fitness Landscape Analysis. CEC 2022: 1-8 - [c77]Ahmed Hassan, Nelishia Pillay:
Hybridizing A Genetic Algorithm With Reinforcement Learning for Automated Design of Genetic Algorithms. CEC 2022: 1-8 - [c76]Ying Ying Liu, Parimala Thulasiraman, Nelishia Pillay:
Bicriterion Coevolution for the Multi-objective Travelling Salesperson Problem. CEC 2022: 1-8 - [c75]Emilio Singh, Nelishia Pillay:
A Study of Transfer Learning in an Ant-Based Generation Construction Hyper-Heuristic. CEC 2022: 1-7 - [c74]Jose M. Tapia-Avitia
A Primary Study on Hyper-Heuristics Powered by Artificial Neural Networks for Customising Population-based Metaheuristics in Continuous Optimisation Problems. CEC 2022: 1-8 - [c73]Nelishia Pillay:
Transfer learning in evolutionary spaces. GECCO Companion 2022: 1038-1045 - [c72]Derrick Beckedahl
Bi-Space Search: Optimizing the Hybridization of Search Spaces in Solving the One Dimensional Bin Packing Problem. ICAISC (2) 2022: 206-217 - [c71]Ahmed Hassan, Nelishia Pillay:
Automated Design of Dynamic Heuristic Set Selection for Cross-Domain Selection Hyper-Heuristics. ICAISC (1) 2022: 219-229 - [c70]Christopher Rajah, Nelishia Pillay
An Improved Structure-Based Partial Solution Search for the Examination Timetabling Problem. ICAISC (2) 2022: 314-326 - [c69]Cry Kuranga
A Comparative Study of Genetic Programming Variants. ICAISC (1) 2022: 377-386 - [c68]Mia Gerber, Nelishia Pillay:
Automated design of feature extraction for unsupervised image clustering using grammatical evolution. SSCI 2022: 30-37 - [c67]Jared O'Reilly
Supplementary-architecture weight-optimization neural networks for advanced weight optimizers. SSCI 2022: 47-54 - [c66]Emilio Singh, Nelishia Pillay:
A Parameter-Based Analysis of Ant-Based Generation Hyper-Heuristics. SSCI 2022: 812-819 - [c65]Ying Ying Liu, Parimala Thulasiraman, Nelishia Pillay:
Worst and Generalized Severity Evaluation for Evolutionary Dynamic Multi-objective Optimization. SSCI 2022: 1012-1020 - 2021
- [j29]George Mweshi, Nelishia Pillay:
An improved grammatical evolution approach for generating perturbative heuristics to solve combinatorial optimization problems. Expert Syst. Appl. 165: 113853 (2021) - [j28]Cry Kuranga
Genetic programming-based regression for temporal data. Genet. Program. Evolvable Mach. 22(3): 297-324 (2021) - [j27]Nelishia Pillay
Assessing hyper-heuristic performance. J. Oper. Res. Soc. 72(11): 2503-2516 (2021) - [j26]Hugo Jair Escalante
Guest Editorial: Automated Machine Learning. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 2887-2890 (2021) - [c64]Jorge M. Cruz-Duarte, Iván Amaya, José Carlos Ortiz-Bayliss, Nelishia Pillay:
Automated Design of Unfolded Metaheuristics and the Effect of Population Size. CEC 2021: 1155-1162 - [c63]Nelishia Pillay, Mia Gerber, Katerina Holan, Steven A. Whitham, Dave K. Berger:
Quantifying the Severity of Common Rust in Maize Using Mask R-CNN. ICAISC (1) 2021: 202-213 - [c62]Emilio Singh
Ant-Based Hyper-Heuristics for the Movie Scene Scheduling Problem. ICAISC (2) 2021: 342-353 - [c61]Luveshan Marimuthu, Nelishia Pillay
A Comparison of Machine Learning Techniques for Diagnosing Multiple Myeloma. ICAISC (2) 2021: 483-494 - [c60]Mia Gerber, Nelishia Pillay, Katerina Holan, Steven A. Whitham, Dave K. Berger:
Automated Hyper-Parameter Tuning of a Mask R-CNN for Quantifying Common Rust Severity in Maize. IJCNN 2021: 1-7 - [c59]Jorge M. Cruz-Duarte, Iván Amaya, José Carlos Ortiz-Bayliss, Nelishia Pillay:
Naïve Hyper-heuristic Online Learning to Generate Unfolded Population-based Metaheuristics to Solve Continuous Optimization Problems. SSCI 2021: 1-8 - [c58]Mia Gerber, Nelishia Pillay, Razia Khammissa:
A Comparative Study of Supervised and Unsupervised Neural Networks for Oral Lesion Detection. SSCI 2021: 1-7 - [c57]Ahmed Hassan, Nelishia Pillay:
An Investigation of Automated Design of VMAF. SSCI 2021: 1-8 - [c56]Kyle W. Pretorius, Nelishia Pillay:
Population based Reinforcement Learning. SSCI 2021: 1-8 - [c55]Darius Scheepers, Nelishia Pillay:
A Study of Transfer Learning in a Generation Constructive Hyper-Heuristic for One Dimensional Bin Packing. SSCI 2021: 1-7 - [c54]Ahmed Hassan, Nelishia Pillay
Dynamic Heuristic Set Selection for Cross-Domain Selection Hyper-heuristics. TPNC 2021: 33-44 - [c53]Thambo Nyathi
On the Transfer Learning of Genetic Programming Classification Algorithms. TPNC 2021: 47-58 - [c52]Emilio Singh
Ant-Based Generation Constructive Hyper-heuristics for the Movie Scene Scheduling Problem. TPNC 2021: 109-120 - [p2]Nelishia Pillay, Thambo Nyathi:
Automated Design of Classification Algorithms. Automated Design of Machine Learning and Search Algorithms 2021: 171-184 - [p1]Nelishia Pillay:
Automated Design (AutoDes): Current Trends and Future Research Directions. Automated Design of Machine Learning and Search Algorithms 2021: 185-187 - [e2]Nelishia Pillay, Rong Qu:
Automated Design of Machine Learning and Search Algorithms. Natural Computing Series, Springer 2021, ISBN 978-3-030-72068-1 [contents] - 2020
- [j25]Rong Qu, Graham Kendall
The General Combinatorial Optimization Problem: Towards Automated Algorithm Design. IEEE Comput. Intell. Mag. 15(2): 14-23 (2020) - [j24]Nelishia Pillay:
The impact of genetic programming in education. Genet. Program. Evolvable Mach. 21(1-2): 87-97 (2020) - [c51]Derrick Beckedahl
A Study of Bi-space Search for Solving the One-Dimensional Bin Packing Problem. ICAISC (2) 2020: 277-289 - [c50]Kyle Pretorious, Nelishia Pillay:
A Comparative Study of Classifiers for Thumbnail Selection. IJCNN 2020: 1-7 - [c49]Cry Kuranga
Nonlinear Regression in Dynamic Environments Using Particle Swarm Optimization. TPNC 2020: 133-144
2010 – 2019
- 2019
- [j23]Nelishia Pillay, Ender Özcan
Automated generation of constructive ordering heuristics for educational timetabling. Ann. Oper. Res. 275(1): 181-208 (2019) - [j22]Ahmed Hassan, Nelishia Pillay:
Hybrid metaheuristics: An automated approach. Expert Syst. Appl. 130: 132-144 (2019) - [c48]Christopher Rajah, Nelishia Pillay:
A Structure-Based Partial Solution Search for the Examination Timetabling Problem. CEC 2019: 81-86 - [c47]George Mweshi
A Grammatical Evolution Approach for the Automated Generation of Perturbative Heuristics. CEC 2019: 2642-2648 - 2018
- [b1]Nelishia Pillay, Rong Qu
Hyper-Heuristics: Theory and Applications. Natural Computing Series, Springer 2018, ISBN 978-3-319-96513-0, pp. 3-101 - [j21]Nelishia Pillay, Rong Qu
Automated Design of Machine Learning and Search Algorithms [Guest Editorial]. IEEE Comput. Intell. Mag. 13(2): 16-17 (2018) - [j20]Anisa W. Ragalo, Nelishia Pillay:
An investigation of dynamic fitness measures for genetic programming. Expert Syst. Appl. 92: 52-72 (2018) - [j19]Thambo Nyathi
Comparison of a genetic algorithm to grammatical evolution for automated design of genetic programming classification algorithms. Expert Syst. Appl. 104: 213-234 (2018) - [j18]Anisa W. Ragalo, Nelishia Pillay:
Evolving dynamic fitness measures for genetic programming. Expert Syst. Appl. 109: 162-187 (2018) - [c46]Ahmed Hassan, Nelishia Pillay:
An Improved Meta-Genetic Algorithm for Hybridizing Metaheuristics. CEC 2018: 1-8 - [c45]Derrick Beckedahl, Andreas Nel, Nelishia Pillay:
A Study of Multi-space Search Optimization. ISDA (1) 2018: 1-9 - [c44]Gisele Lobo Pappa, Michael T. M. Emmerich
Tutorials at PPSN 2018. PPSN (2) 2018: 477-489 - [c43]Thambo Nyathi
Automated Design of Genetic Programming Classification Algorithms for Financial Forecasting Using Evolutionary Algorithms. TPNC 2018: 201-214 - [r1]Marc Sevaux, Kenneth Sörensen
Adaptive and Multilevel Metaheuristics. Handbook of Heuristics 2018: 3-21 - 2017
- [c42]Nelishia Pillay, Derrick Beckedahl
EvoHyp - a Java toolkit for evolutionary algorithm hyper-heuristics. CEC 2017: 2706-2713 - [c41]Ahmed Hassan, Nelishia Pillay:
A Meta-Genetic Algorithm for Hybridizing Metaheuristics. EPIA 2017: 369-381 - [c40]Thambo Nyathi
Automated Design of Genetic Programming Classification Algorithms Using a Genetic Algorithm. EvoApplications (2) 2017: 224-239 - 2016
- [j17]Nelishia Pillay:
A review of hyper-heuristics for educational timetabling. Ann. Oper. Res. 239(1): 3-38 (2016) - [j16]Nelishia Pillay:
Incorporating chaos into the developmental approach for solving the examination timetabling problem. Int. J. Bio Inspired Comput. 8(6): 355-366 (2016) - [c39]Nelishia Pillay:
Evolving construction heuristics for the curriculum based university course timetabling problem. CEC 2016: 4437-4443 - [c38]Carola Doerr
Tutorials at PPSN 2016. PPSN 2016: 1012-1022 - [c37]Nomzamo Ntombela, Nelishia Pillay:
Evolving Construction Heuristics for the Symmetric Travelling Salesman Problem. SAICSIT 2016: 30:1-30:5 - [e1]Nelishia Pillay, Andries P. Engelbrecht, Ajith Abraham, Mathys C. du Plessis, Václav Snásel, Azah Kamilah Muda:
Advances in Nature and Biologically Inspired Computing - Proceedings of the 7th World Congress on Nature and Biologically Inspired Computing (NaBIC 2015) in Pietermaritzburg, South Africa, held December 01-03, 2015. Advances in Intelligent Systems and Computing 419, Springer 2016, ISBN 978-3-319-27399-0 [contents] - 2015
- [j15]Nelishia Pillay:
Intelligent System Design Using Hyper-Heuristics. South Afr. Comput. J. 56 (2015) - [c36]Clive Frankland, Nelishia Pillay:
Evolving game playing strategies for Othello. CEC 2015: 1498-1504 - [c35]Christopher Rajah, Nelishia Pillay:
Combining Development and Evolution. IJCCI (ECTA) 2015: 188-195 - [c34]Kevin Igwe, Nelishia Pillay:
A Study of Genetic Programming and Grammatical Evolution for Automatic Object-Oriented Programming: A Focus on the List Data Structure. NaBIC 2015: 151-163 - [c33]Clive Frankland, Nelishia Pillay:
Evolving Heuristic Based Game Playing Strategies for Checkers Incorporating Reinforcement Learning. NaBIC 2015: 165-178 - [c32]Ahmed Hassan, Nelishia Pillay:
A Hyper-Heuristic Approach to Solving the Ski-Lodge Problem. NaBIC 2015: 201-210 - [c31]Nelishia Pillay:
A Generative Hyper-Heuristic for Deriving Heuristics for Classical Artificial Intelligence Problems. NaBIC 2015: 337-346 - [c30]Clive Frankland, Nelishia Pillay:
Evolving Game Playing Strategies for Othello Incorporating Reinforcement Learning and Mobility. SAICSIT 2015: 16:1-16:9 - 2014
- [j14]Nelishia Pillay:
A survey of school timetabling research. Ann. Oper. Res. 218(1): 261-293 (2014) - [j13]Gerhard Post, Jeffrey H. Kingston, Samad Ahmadi, Sophia Daskalaki, Christos Gogos
XHSTT: an XML archive for high school timetabling problems in different countries. Ann. Oper. Res. 218(1): 295-301 (2014) - [c29]Anisa W. Ragalo, Nelishia Pillay:
A Hyper-heuristic approach towards mitigating Premature Convergence caused by the objective fitness function in GP. ISDA 2014: 68-75 - [c28]Emmanuel Dufourq
Hybridizing evolutionary algorithms for creating classifier ensembles. NaBIC 2014: 84-90 - 2013
- [j12]Anisa W. Ragalo, Nelishia Pillay:
Improved evolvability in genetic programming with polyandry. South Afr. Comput. J. 51: 22-43 (2013) - [c27]Nelishia Pillay, P. Govender:
Constrained Minimum-Variance PID Control using Hybrid Nelder-Mead Simplex and Swarm Intelligence. ICAART (2) 2013: 330-337 - [c26]Rushil Raghavjee, Nelishia Pillay:
A Study of Genetic Algorithms to Solve the School Timetabling Problem. MICAI (2) 2013: 64-80 - [c25]Kevin Igwe, Nelishia Pillay, Christopher Rae:
Solving the 8-puzzle problem using genetic programming. SAICSIT 2013: 64-67 - [c24]Nelishia Pillay:
A comparative study of hyper-heuristics for solving the school timetabling problem. SAICSIT 2013: 278-285 - 2012
- [j11]Nelishia Pillay:
Evolving hyper-heuristics for the uncapacitated examination timetabling problem. J. Oper. Res. Soc. 63(1): 47-58 (2012) - [j10]Nelishia Pillay:
A study of evolutionary algorithm selection hyper-heuristics for the one-dimensional bin-packing problem. South Afr. Comput. J. 48: 31-40 (2012) - [j9]Nelishia Pillay:
Finding solutions to sudoku puzzles using human intuitive heuristics. South Afr. Comput. J. 49: 25-34 (2012) - [c23]Rushil Raghavjee, Nelishia Pillay:
A comparison of genetic algorithms and genetic programming in solving the school timetabling problem. NaBIC 2012: 98-103 - [c22]Anisa W. Ragalo, Nelishia Pillay:
A building block conservation and extension mechanism for improved performance in Polynomial Symbolic Regression tree-based Genetic Programming. NaBIC 2012: 123-129 - [c21]Christopher Rae, Nelishia Pillay:
A preliminary study into the use of an evolutionary algorithm hyper-heuristic to solve the nurse rostering problem. NaBIC 2012: 156-161 - 2011
- [c20]Rushil Raghavjee, Nelishia Pillay:
The effect of construction heuristics on the performance of a genetic algorithm for the school timetabling problem. SAICSIT 2011: 187-194 - 2010
- [j8]Nelishia Pillay, Wolfgang Banzhaf:
An informed genetic algorithm for the examination timetabling problem. Appl. Soft Comput. 10(2): 457-467 (2010) - [c19]Rushil Raghavjee, Nelishia Pillay:
Using genetic algorithms to solve the South African school timetabling problem. NaBIC 2010: 286-292 - [c18]Rosanne Els, Nelishia Pillay:
An evolutionary algorithm hyper-heuristic for producing feasible timetables for the curriculum based university course timetabling problem. NaBIC 2010: 460-466 - [c17]Nelishia Pillay:
An empirical study into the structure of heuristic combinations in an evolutionary algorithm hyper-heuristic for the examination timetabling problem. SAICSIT 2010: 251-257 - [c16]Nelishia Pillay:
A study into the use of hyper-heuristics to solve the school timetabling problem. SAICSIT 2010: 258-264 - [c15]Rushil Raghavjee, Nelishia Pillay:
An informed genetic algorithm for the high school timetabling problem. SAICSIT 2010: 408-412
2000 – 2009
- 2009
- [j7]Nelishia Pillay, Wolfgang Banzhaf
A study of heuristic combinations for hyper-heuristic systems for the uncapacitated examination timetabling problem. Eur. J. Oper. Res. 197(2): 482-491 (2009) - [j6]Nelishia Pillay:
Learning difficulties experienced by students in a course on formal languages and automata theory. ACM SIGCSE Bull. 41(4): 48-52 (2009) - [c14]Rushil Raghavjee, Nelishia Pillay:
Evolving Solutions to the School Timetabling Problem. NaBIC 2009: 1524-1527 - [c13]Nelishia Pillay:
The revised developmental approach to the uncapacitated examination timetabling problem. SAICSIT 2009: 187-192 - 2008
- [j5]Nelishia Pillay:
Teaching the theory of formal languages and automata in the Computer Science undergraduate curriculum. South Afr. Comput. J. 42: 87-94 (2008) - [c12]Amashini Naidoo, Nelishia Pillay:
Using Genetic Programming for Turing Machine Induction. EuroGP 2008: 350-361 - [c11]Nelishia Pillay, Wolfgang Banzhaf
A Developmental Approach to the Uncapacitated Examination Timetabling Problem. PPSN 2008: 276-285 - [c10]Nelishia Pillay:
An analysis of representations for hyper-heuristics for the uncapacitated examination timetabling problem in a genetic programming system. SAICSIT 2008: 188-192 - [c9]Rushil Raghavjee, Nelishia Pillay:
An application of genetic algorithms to the school timetabling problem. SAICSIT 2008: 193-199 - 2007
- [j4]Wolfgang Banzhaf
Why complex systems engineering needs biological development. Complex. 13(2): 12-21 (2007) - [c8]Nelishia Pillay, Wolfgang Banzhaf:
A Genetic Programming Approach to the Generation of Hyper-Heuristics for the Uncapacitated Examination Timetabling Problem. EPIA Workshops 2007: 223-234 - [c7]Amashini Naidoo, Nelishia Pillay:
The Induction of Finite Transducers Using Genetic Programming. EuroGP 2007: 371-380 - [c6]Amashini Naidoo, Nelishia Pillay:
Evolving pushdown automata. SAICSIT 2007: 83-90 - [c5]Nelishia Pillay, Caryl K. A. Chalmers:
A hybrid approach to automatic programming for the object-oriented programming paradigm. SAICSIT 2007: 116-124 - 2005
- [j3]Nelishia Pillay, Vikash R. Jugoo:
An investigation into student characteristics affecting novice programming performance. ACM SIGCSE Bull. 37(4): 107-110 (2005) - [c4]Nelishia Pillay:
An investigation into using genetic programming as a means of inducing solutions to novice procedural programming problems. GECCO 2005: 1781-1782 - 2004
- [j2]Nelishia Pillay:
A first course in genetic programming. ACM SIGCSE Bull. 36(4): 93-96 (2004) - [c3]Tanja Reinhardt, Nelishia Pillay:
Analysis of Spreadsheet Errors Made by Computer Literacy Students. ICALT 2004 - [c2]Nelishia Pillay:
A first course in genetic programming. ITiCSE-WGR 2004: 93-96 - 2003
- [j1]Nelishia Pillay:
Developing intelligent programming tutors for novice programmers. ACM SIGCSE Bull. 35(2): 78-82 (2003) - 2002
- [c1]Nelishia Pillay:
Using genetic programming for the induction of novice procedural programming solution algorithms. SAC 2002: 578-583
Coauthor Index
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