An introduction to software agents
Software agents
On agent-based software engineering
Artificial Intelligence
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Comparing Synchronous and Asynchronous Cellular Genetic Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
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The paper explores connections between population topology and individual interactions inducing autonomy, communication and learning. A Collaborative Asynchronous Multi-Population Evolutionary (CAME)model is proposed. Each individual in the population acts as an autonomous agent with the goal of optimizing its fitness being able to communicate and select a mate for recombination. Different strategies for recombination correspond to different societies of agents (subpopulations). The asynchronous search process is facilitated by a gradual propagation of the fittest individuals' genetic material into the population. Furthermore, two heuristics are proposed for avoiding local optima and for maintaining population diversity. These are the dynamic dominance heuristic and the shaking mechanism, both being integrated in the CAME model. Numerical results indicate a good performance of the proposed evolutionary asynchronous search model. Particularly, proposed CAME technique obtains excellent results for difficult highly multimodal optimization problems indicating a huge potential for dynamic and multicriteria optimization.