In the domain of the complex control problems for agents, neuroevolution, i.e. artificial evolution
of neural networks, methods have been continuously shown to offer high performance solutions
which may be unpredictable by external controller design. Recent studies have proved
that these methods can also be successfully applied for cooperative multi-agent systems to
evolve the desired team behavior. For a given task which may benefit from both cooperation
and behavioral specialization, the genetic diversity of the team members may have important
effects on the team performance. In this thesis, the single prey hunting problem is chosen
as the case, where the performance of the evolved predator teams with distinct genetic similarity
levels are systematically examined. For this purpose, three similarity levels, namely
homogeneous, partially heterogeneous and heterogeneous, are adopted and analyzed in various
problem-specific and algorithmic settings. Our similarity levels differ from each other in
terms of the number of groups of identical agents in a single predator team, where identicalness
of two agents refers to the fact that both have the same synaptic weight vector in their
neural network controllers. On the other hand, the problem-specific conditions comprise three
different fields of vision for predators, whereas algorithmic settings refer to varying number of individuals in the populations, as well as two different selection levels such as team and group levels. According to the experimental results within a simulated grid environment, we
show that different genetic similarity level-field of vision-algorithmic setting combinations
beget different performance results.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12610832/index.pdf |
| Date | 01 August 2009 |
| Creators | Yalcin, Cagri |
| Contributors | Sehitoglu, Onur Tolga |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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