Abstract: Hybridizing agent-based paradigm with evolutionary computation can enhance the field of meta-heuristics in a significant way, giving to usually passive individuals autonomy and capabilities of perception and interaction with other ones, treating them as agents. In the paper as a follow-up to the previous research, an evolutionary multi-agent system (EMAS) is examined in difficult discrete benchmark problems. As a means for comparison, classical evolutionary algorithm (constructed along with Michalewicz model) implemented in island-model is used. The results encourage for further research regarding application of EMAS in discrete problem domain.

Abstract: Agents provide a feasible mean for maintaining and manipulating large scale data. This paper deals with the problem of information exchange between different agents. It uses graph based formalism for the representation of knowledge maintained by an agent and graph transformations as a mean of knowledge exchange. Such a rigorous formalism ensures the cohesion of graph-based knowledge held by agents after each modification and exchange action. The approach presented in this paper is illustrated by a case study dealing with the problem of personal data held in different places (maintained by different agents) and the process of transmitting such information

Abstract: In this paper we propose an agent-based system for Service-Oriented Architecture self-adaptation. Services are supervised by autonomous agents which are responsible for deciding which service should be chosen for interoperation. Agents learn the choice strategy autonomously using supervised learning. In experiments we show that supervised learning (Naive Bayes, C4.5 and Ripper) allows to achieve much better efficiency than simple strategies such as random choice or round robin. What is also important, supervised learning generates a knowledge in a readable form, which may be analyzed by experts.

Abstract: Metaheuristics resulting from the hybridization of multi-agent systems with evolutionary computing are efficient in many optimization problems. Evolutionary multi-agent systems (EMAS) are more similar to biological evolution than classical evolutionary algorithms. However, technological limitations prevented the use of fully asynchronous agents in previous EMAS implementations. In this paper we present a new algorithm for agent-based evolutionary computations. The individuals are represented as fully autonomous and asynchronous agents. Evolutionary operations are performed continuously and no artificial generations need to be distinguished. Our results show that such asynchronous evolutionary operators and the resulting absence of explicit generations lead to significantly better results. An efficient implementation of this algorithm was possible through the use of Erlang technology, which natively supports lightweight processes and asynchronous communication.

Abstract: In this paper we present a hypergraph grammar based multi-frontal solver for three dimensional grids with point singularities. We show experimentally that the computational cost of the resulting solver algorithm is linear with respect to the number of degrees of freedom. We also propose a reutilization algorithm that enables to reuse LU factorizations over unrefined parts of the mesh when new local refinements are executed by the hypergraph grammar productions.