Christiaan Scheepers

Orcid: 0000-0002-3285-7901

According to our database1, Christiaan Scheepers authored at least 12 papers between 2014 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
The Porcupine Measure for Comparing the Performance of Multi-Objective Optimization Algorithms.
Algorithms, June, 2023

2019
Multi-guide particle swarm optimization for multi-objective optimization: empirical and stability analysis.
Swarm Intell., 2019

2018
Stability Analysis of the Multi-objective Multi-guided Particle Swarm Optimizer.
Proceedings of the Swarm Intelligence - 11th International Conference, 2018

2017
Quantified pareto-optimal front comparisons using attainment surfaces.
Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Vector evaluated particle swarm optimization: The archive's influence on performance.
Proceedings of the 2017 IEEE Congress on Evolutionary Computation, 2017

2016
Training multi-agent teams from zero knowledge with the competitive coevolutionary team-based particle swarm optimiser.
Soft Comput., 2016

Vector evaluated particle swarm optimization archive management: Pareto optimal front diversity sensitivity analysis.
Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016

Misleading Pareto optimal front diversity metrics: Spacing and distribution.
Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence, 2016

Vector evaluated particle swarm optimization exploration behavior part II: Quantitative analysis.
Proceedings of the IEEE Congress on Evolutionary Computation, 2016

Vector evaluated particle swarm optimization exploration behavior part I: Explorative analysis.
Proceedings of the IEEE Congress on Evolutionary Computation, 2016

2014
Analysis of stagnation behaviour of competitive coevolutionary trained neuro-controllers.
Proceedings of the 2014 IEEE Symposium on Swarm Intelligence, 2014

Competitive coevolutionary training of simple soccer agents from zero knowledge.
Proceedings of the IEEE Congress on Evolutionary Computation, 2014


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