Abstract. The paper proposes a JADE-based A-Team environment (JADE-A-Team) as a middleware supporting the construction of the dedicated A-Team architectures used for solving variety of computationally hard optimization problems. The paper includes a general overview of the functionality and structure of the proposed environment and a more detailed description of optimization agents including their standard functions, ontology, construction requirements and activation procedure. Further sections explain how to create and activate an A-Team agent and how the communication between agents is handled. Conclusions focus on advantages of the JADE-A-Team environment and on suggestions for further research.
The paper investigates a Gene Expression Programming (GEP)-based ensemble classifier constructed using the stacked generalization concept. The classifier has been implemented with a view to enable parallel processing with the use of Spark and SWIM — an open source genetic programming library. The classifier has been validated in computational experiments carried out on benchmark datasets. Also, it has been inbvestigated how the results are influenced by some settings. The paper is an extension of a previous paper of the authors.
Abstract. JABAT supports designing and implementing A-Team architectures for solving difficult optimization problems. This paper presents several applications of JABAT as a tool for solving such problems. List of implementations and extensions of JABAT shows how useful and flexible the system can be. The paper summarises experiences of authors gained while developing various A-Teams. Some conclusions concerning such details of the A-Team model like the composition of the team of agents, the choice of rules determining how the agents interact with the population of solutions, or how synchronisation or cooperation of agents influence the quality of results are offered.
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