ALIFE Proceedings of the sixth international conference on Artificial life
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Active vision and feature selection in evolutionary behavioral systems
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Robot Error Detection Using an Artificial Immune System
EH '03 Proceedings of the 2003 NASA/DoD Conference on Evolvable Hardware
Decentralized control system for autonomous navigation based on an evolved artificial immune network
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Hybrid methods using genetic algorithms for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolution of homing navigation in a real mobile robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The balance between initial training and lifelong adaptation in evolving robot controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Idiotypic Immune Networks in Mobile-Robot Control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A combined short-term learning (STL) and long-term learning (LTL) approach to solving mobile-robot navigation problems is presented and tested in both the real and virtual domains. The LTL phase consists of rapid simulations that use a genetic algorithm to derive diverse sets of behaviours, encoded as variable sets of attributes, and the STL phase is an idiotypic artificial immune system. Results from the LTL phase show that sets of behaviours develop very rapidly, and significantly greater diversity is obtained when multiple autonomous populations are used, rather than a single one. The architecture is assessed under various scenarios, including removal of the LTL phase and switching off the idiotypic mechanism in the STL phase. The comparisons provide substantial evidence that the best option is the inclusion of both the LTL phase and the idiotypic system. In addition, this paper shows that structurally different environments can be used for the two phases without compromising transferability.