Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Agent-Oriented Model of Simulated Evolution
SOFSEM '02 Proceedings of the 29th Conference on Current Trends in Theory and Practice of Informatics: Theory and Practice of Informatics
Formal model for agent-based asynchronous evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Asymptotic analysis of computational multi-agent systems
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
An agent-based model of hierarchic genetic search
Computers & Mathematics with Applications
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The refined model for the biologically inspired agent-based computation systems EMAS and iEMAS conforming to the BDI standard is presented. Moreover, their evolution is expressed in the form of the stationary Markov chains. This paper generalizes the results obtained by Byrski and Schaefer [7] to a strongly desired case in which some agents' actions can be executed in parallel. In order to find the Markov transition rule, the precise synchronization scheme was introduced, which allows to establish the stepwise stochastic evolution of the system. The crucial feature which allows to compute the probability transition function in case of parallel execution of local actions is the commutativity of their transition operators. Some abstract conditions expressing such a commutativity which allow to classify the agents' actions as local or global are formulated and verified in a very simple way. The above-mentioned Markov model constitutes the basis of the asymptotic analysis of EMAS and iEMAS necessary to evaluate their search possibilities and efficiency.