Inproceedings,
Shared memory based Cooperative Coevolution
Proceedings of the 1998 IEEE World Congress on Computational Intelligence, page 570--574. Anchorage, Alaska, USA, IEEE Press, (5-9 May 1998)
Abstract
Autonomous agents that possess distinct expertise but
lack proper coordination skills can suffer from poor
performance in a cooperative setting. The success of
agents in multiagent systems is based on their ability
to adapt effectively with other agents in completing
their tasks. We present here a co-evolutionary approach
to generating behavioral strategies for autonomous
agents cooperating with each other to achieve a common
goal. We co-evolve agent behaviors with genetic
algorithms (GAS) where one GA population is evolved per
individual in the cooperative group. Groups are formed
by pairing strategies from each population and the best
pairs are stored in shared memory. Population members
are evaluated by pairing them with representatives of
other populations in the shared memory. Experimental
results obtained by conducting experiments in a room
painting domain are presented, showing the success of
the shared memory approach in consistently generating
optimal behavior patterns. Performance comparisons with
a random pairing approach and a single population
approach demonstrate the utility of the shared memory
approach.
Tags
- algorithms
- algorithms,
- and
- applications
- comparing
- computation,
- evolutionary
- genetic
- nn
- of
- operators,
- programming,
- representation
