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Mathys C. du Plessis
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2020 – today
- 2023
- [j16]Nathan Watt, Mathys C. du Plessis
:
Neuro-augmented vision for evolutionary robotics. Mach. Vis. Appl. 34(5): 95 (2023) - [j15]Joshua Oladipo
Congestion aware ant colony optimisation algorithm for routing and spectrum assignment in flexi-grid optical burst switching networks. Photonic Netw. Commun. 45(2): 67-78 (2023) - 2022
- [j14]Nathan Watt
Towards robot vision using deep neural networks in evolutionary robotics. Evol. Intell. 15(1): 81-97 (2022) - [c27]Brydon A. Leonard, Mathys C. du Plessis:
Bootstrapped Neuro-Simulation for Damage Recovery in Complex Robots. CEC 2022: 1-8 - 2021
- [j13]Grant W. Woodford, Mathys C. du Plessis:
Bootstrapped Neuro-Simulation for complex robots. Robotics Auton. Syst. 136: 103708 (2021) - [c26]Antin Phillips, Mathys C. du Plessis:
Adaptive Navigation Strategies in Evolutionary Robotics Controllers with Location Perception. CEC 2021: 2499-2506 - [c25]Mathys C. du Plessis
Revisiting the Use of Noise in Evolutionary Robotics. SACAIR 2021: 211-226 - [c24]Bouwer Botha, Mathys C. du Plessis
Avoiding Unexpected Obstacles During Robotic Navigation Using Rapidly-Exploring Random Trees and a Neural Network Simulator. SACAIR 2021: 227-241 - 2020
- [j12]Antin Phillips, Mathys C. du Plessis:
Evolutionary Robotics Controllers with Proprioception Facilitated by Neural-Network-Based Simulators. Artif. Life 26(4): 455-483 (2020) - [j11]Brydon A. Leonard
Bootstrapped Neuro-Simulation as a method of concurrent neuro-evolution and damage recovery. Robotics Auton. Syst. 124: 103398 (2020) - [j10]Grant W. Woodford
Complex Morphology Neural Network Simulation in Evolutionary Robotics. Robotica 38(5): 886-902 (2020)
2010 – 2019
- 2019
- [j9]Christiaan J. Pretorius
Evolutionary Robotics Applied to Hexapod Locomotion: a Comparative Study of Simulation Techniques. J. Intell. Robotic Syst. 96(3-4): 363-385 (2019) - [c23]Nathan Watt, Mathys C. du Plessis:
Dropout for Recurrent Neural Networks. INNSBDDL 2019: 38-47 - [c22]Antin Phillips, Mathys C. du Plessis:
Towards the Incorporation of Proprioception in Evolutionary Robotics Controllers. IRC 2019: 226-229 - [c21]François Du Plessis, Mathys C. du Plessis, Tim Gibbon
Ant Colony Optimization on an OBS Network with Link Cost and Impairments. ISDA 2019: 456-465 - 2018
- [c20]Grant W. Woodford, Mathys C. du Plessis:
Robotic snake simulation using ensembles of artificial neural networks in evolutionary robotics. GECCO 2018: 173-180 - [c19]Nathan Watt
Dropout algorithms for recurrent neural networks. SAICSIT 2018: 72-78 - 2017
- [j8]Christiaan J. Pretorius
Neuroevolution of Inverted Pendulum Control: A Comparative Study of Simulation Techniques. J. Intell. Robotic Syst. 86(3-4): 419-445 (2017) - [j7]Andrew S. Gravett
A distributed ant-based algorithm for routing and wavelength assignment in an optical burst switching flexible spectrum network with transmission impairments. Photonic Netw. Commun. 34(3): 375-395 (2017) - [j6]Grant W. Woodford, Mathys C. du Plessis, Christiaan J. Pretorius
Concurrent controller and Simulator Neural Network development for a snake-like robot in Evolutionary Robotics. Robotics Auton. Syst. 88: 37-50 (2017) - [c18]Joshua Oladipo, Mathys C. du Plessis, Timothy B. Gibbon
Implementation and validation of an Omnet++ optical burst switching simulator. PEMWN 2017: 1-6 - [c17]Christiaan J. Pretorius
The transferability of evolved hexapod locomotion controllers from simulation to real hardware. RCAR 2017: 567-574 - 2016
- [j5]Grant W. Woodford, Christiaan J. Pretorius
Concurrent controller and Simulator Neural Network development for a differentially-steered robot in Evolutionary Robotics. Robotics Auton. Syst. 76: 80-92 (2016) - [c16]Andrew S. Gravett, Mathys C. du Plessis, Timothy B. Gibbon
Hybridising Ant Colony Optimisation with a upper confidence bound algorithm for routing and wavelength assignment in an optical burst switching network. SSCI 2016: 1-8 - [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
- [c15]Mathys C. du Plessis, Andries P. Engelbrecht, André P. Calitz:
Self-Adapting the Brownian Radius in a Differential Evolution Algorithm for Dynamic Environments. FOGA 2015: 114-128 - [c14]Michael W. Louwrens, Mathys C. du Plessis, Jéan H. Greyling:
Using Standard Components in Evolutionary Robotics to Produce an Inexpensive Robot Arm. NaBIC 2015: 129-139 - [c13]Grant W. Woodford, Mathys C. du Plessis, Christiaan J. Pretorius
Evolving Snake Robot Controllers Using Artificial Neural Networks as an Alternative to a Physics-Based Simulator. SSCI 2015: 267-274 - 2014
- [c12]Christiaan J. Pretorius
A comparison of neural networks and physics models as motion simulators for simple robotic evolution. IEEE Congress on Evolutionary Computation 2014: 2793-2800 - 2013
- [j4]Mathys C. du Plessis, Andries P. Engelbrecht
Differential evolution for dynamic environments with unknown numbers of optima. J. Glob. Optim. 55(1): 73-99 (2013) - [j3]Christiaan J. Pretorius
Simulating Robots Without Conventional Physics: A Neural Network Approach. J. Intell. Robotic Syst. 71(3-4): 319-348 (2013) - [c11]Clayton Burger, Mathys C. du Plessis, Charmain Cilliers:
Design and parametric considerations for artificial neural network pruning in UCT game playing. SAICSIT 2013: 209-217 - [p1]Mathys C. du Plessis, Andries P. Engelbrecht
Self-Adaptive Differential Evolution for Dynamic Environments with Fluctuating Numbers of Optima. Metaheuristics for Dynamic Optimization 2013: 117-145 - 2012
- [j2]Mathys C. du Plessis, Andries P. Engelbrecht
Using Competitive Population Evaluation in a differential evolution algorithm for dynamic environments. Eur. J. Oper. Res. 218(1): 7-20 (2012) - 2011
- [c10]Clayton Burger, Mathys C. du Plessis:
Does Chomsky complexity affect genetic programming computational requirements? SAICSIT 2011: 31-39 - [c9]Mathys C. du Plessis, Andries P. Engelbrecht
Self-adaptive competitive differential evolution for dynamic environments. SDE 2011: 41-48 - 2010
- [c8]Christiaan J. Pretorius
A Neural Network-based kinematic and light-perception simulator for simple robotic evolution. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c7]Meredith A. Barnes, Mathys C. du Plessis, Brenda Scholtz
Toward database inference by GUI analysis: a case study. SAICSIT 2010: 346-349
2000 – 2009
- 2009
- [j1]Mathys C. du Plessis, Gideon de V. de Kock:
Incorporating structured text retrieval into the extended Boolean model. Comput. Informatics 28(5): 581-597 (2009) - [c6]Mathys C. du Plessis:
A hybrid neural network and Minimax algorithm for zero-sum games. SAICSIT 2009: 54-59 - [c5]Christiaan J. Pretorius
Towards an artificial neural network-based simulator for behavioural evolution in evolutionary robotics. SAICSIT 2009: 170-178 - 2008
- [c4]Mathys C. du Plessis, Andries P. Engelbrecht
Improved differential evolution for dynamic optimization problems. IEEE Congress on Evolutionary Computation 2008: 229-234 - [c3]Mathys C. du Plessis, Lynette Barnard:
Incorporating layout managers into an evolutionary programming algorithm to design graphical user interfaces. SAICSIT 2008: 41-47 - [c2]Gopal Ravi Sankar, Jéan H. Greyling, Dieter Vogts, Mathys C. du Plessis:
Models towards a hybrid conversational agent for contact centres. SAICSIT 2008: 200-209 - 2004
- [c1]Janet Wesson
A ZoomTree interface for searching genealogical information. Afrigraph 2004: 131-136
Coauthor Index
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