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Stochastic Dynamics of Evolutionary Multi-Agent Systems

Byrski A., Schaefer R.

Prace Naukowe Politechniki Warszawskiej. Elektronika

2009

z. 169

27-34

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 the behavior of the proposed complex systems as well as their limitations.

Agent-based optimization of neural classifiers

Byrski A., Dobrowolski J., Toboła K.

Prace Naukowe Politechniki Warszawskiej. Elektronika

2008

z. 165

59-65

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 are presented.

Mechanism of 'crowd' in evolutionary MAS for multiobjective optimisation

Kisiel-Dorohinicki M.

Prace Naukowe Politechniki Warszawskiej. Elektronika

2002

z. 139

63--74

This work presents a new evolutionary approach to the search for a global solution (in the Pareto sense) to the multiobjective optirnisation problem. The novelty of the method proposed consists in the application of an evolutionary multi-agent system (EMAS) instead of classical evolutionary algorithms. Decentralisation of the evolutionary process in EMAS allows for intensive exploration of the search space, and the introduced mechanism of 'crowd' allows for effective approximation of the whole Pareto frontier. In the chapter the technique is described and preliminary experimental results are reported.