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This paper tackles the application of evolutionary multi-agent computing to solve inverse problems. High costs of fitness function call become a major difficulty when approaching these problems with population-based heuristics. However, evolutionary agent-based systems (EMAS) turn out to reduce the fitness function calls, which makes them a possible weapon of choice against them. This paper recalls...
The model for the biologically inspired agent-based computation systems EMAS and iEMAS conformed to BDI standard is presented. System dynamics was modeled as the stationary Markov chain. The space of states and transition functions were identified. The probability transition of the whole system is composed of the conditional transitions caused by the particular actions. Such a model allows for better...
The refined model for the biologically inspired agent-based computation system EMAS conformed to BDI standard is presented. The considerations are based on the model of the system dynamics as the stationary Markov chain already presented. In the course of paper space of the system states is modified in order assure state coherency and set of actions is simplified. Such a model allows for better understanding...
In the paper an agent-based system of evolving neural networks dedicated to solving classification problem is presented. Next, aspects of the system concerning management of collective intelligence and evolution of parameters of neural network are discussed. Evolutionary multi-agent system (EMAS) is described with enhanced immune-inspired selection mechanism. Finally selected results of the experiments...
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