The Baldwin effect in the immune system: learning by somatic hypermutation
Adaptive individuals in evolving populations
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
Learning to Play Chess Using Temporal Differences
Machine Learning
Computers play the beer game: can artificial agents manage supply chains?
Decision Support Systems - Special issue: Formal modeling and electronic commerce
A Memetic Approach to the Nurse Rostering Problem
Applied Intelligence
On Evolving Robust Strategies for Iterated Prisoner's Dilemma
AI '93/AI '94 Selected papers from the AI'93 and AI'94 Workshops on Evolutionary Computation, Process in Evolutionary Computation
Incremental Learning with Partial Instance Memory
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
The Artificial Evolution of Cooperation
AE '95 Selected Papers from the European conference on Artificial Evolution
Complete Classes of Strategies for the Classical Iterated Prisoner's Dilemma
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Belief revision via Lamarckian evolution
New Generation Computing
Evolving behaviors in the iterated prisoner's dilemma
Evolutionary Computation
Change your tags fast! - a necessary condition for cooperation?
MABS'04 Proceedings of the 2004 international conference on Multi-Agent and Multi-Agent-Based Simulation
Speciation as automatic categorical modularization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Behavioral diversity, choices and noise in the iterated prisoner's dilemma
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Evolving subjective utilities: Prisoner's Dilemma game examples
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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This paper examines the comparative performance and adaptability of evolutionary, learning, and memetic strategies to different environment settings in the Iterated Prisoner's Dilemma (IPD). A memetic adaptation framework is developed for IPD strategies to exploit the complementary features of evolution and learning. In the paradigm, learning serves as a form of directed search to guide evolving strategies to attain eventual convergence towards good strategy traits, while evolution helps to minimize disparity in performance among learning strategies. Furthermore, a double-loop incremental learning scheme (ILS) that incorporates a classification component, probabilistic update of strategies and a feedback learning mechanism is proposed and incorporated into the evolutionary process. A series of simulation results verify that the two techniques, when employed together, are able to complement each other's strengths and compensate for each other's weaknesses, leading to the formation of strategies that will adapt and thrive well in complex, dynamic environments.