Pareto-Dominance Based MOGP for Evolving Soccer Agents
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- @InProceedings{Lazarus:2015:ieeeSSCI,
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author = "Christopher Lazarus",
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booktitle = "IEEE Symposium Series on Computational Intelligence",
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title = "Pareto-Dominance Based MOGP for Evolving Soccer
Agents",
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year = "2015",
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pages = "280--287",
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abstract = "Robot behaviour generation is an attractive option to
automatically produce robot controllers. Most
high-level robot behaviours comprise multiple
objectives that may be conflicting with each other.
This research describes experiments using two
Pareto-dominance based algorithms together with a
Multiobjective Genetic Programming (MOGP) framework to
evolve high-level robot behaviours using only primitive
commands. The performance of hand-coded controllers are
compared against controllers evolved using the
Non-dominated Sorting Genetic Algorithm II (NSGA-II)
and Strength Pareto Evolutionary Algorithm 2 (SPEA2)
algorithms. An additional comparison is also performed
against controllers evolved using the weighted sum
fitness function. The experiment results show that the
Pareto dominance based MOGP performed better than the
hand-coded and the weighted sum evolved controllers.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI.2015.49",
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month = dec,
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notes = "Also known as \cite{7376622}",
- }
Genetic Programming entries for
Christopher Lazarus
Citations