By Aleksander Byrski, Marek Kisiel-Dorohinicki
This publication addresses agent-based computing, concentrating specifically on evolutionary multi-agent platforms (EMAS), that have been constructed because 1996 on the AGH collage of technology and know-how in Cracow, Poland. It presents the appropriate historical past info on and a close description of this computing paradigm, besides key experimental results.
Readers will enjoy the insightful dialogue, which essentially matters the effective implementation of computing frameworks for constructing EMAS and related computing structures, in addition to an in depth formal version. Theoretical deliberations demonstrating that computing with EMAS continuously is helping to discover the optimum answer also are incorporated, rounding out the coverage.
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Additional resources for Evolutionary Multi-Agent Systems: From Inspirations to Applications
A new operator – migration – controls the process of exchanging individuals between regions (see Fig. 2). The model is usually described by a few parameters: a number of regions, a number of individuals in each region, as well as migration topology, rate/interval and a strategy of choosing individuals to migrate. Migration topology describes how individuals migrate from one region to another. This often depends on software architecture and the most common are hypercube, ring, or k-clique. In an island model individuals can migrate to any other subpopulation, while in a stepping stone model individuals can migrate only to neighbouring region(s).
For parallel Simple Genetic Algorithm in , as an extension of the works by Vose. Apart from these systems, more advanced search and optimization techniques like memetic or agent-based computational systems lack such models, with some notable exceptions. , asymptotically finding one of the best k solutions in the search space, where k is the population size). , strategies that use no feedback, check all possible memes and pick the best one, or use a complete historical knowledge to decide on the choice of meme respectively) are globally convergent using elitist selection mechanisms.
Parallel evolutionary algorithm). The computing systems discussed in this monograph cross two classes: LTH and HTH. g. parallel evolutionary algorithm consisting of identically configured islands vs. differently parametrized or completely different algorithms running on the islands). g. evolutionary algorithm vs. coevolutionary algorithm). g. memetic algorithm vs. meta-evolution ). Following this classification, the systems discussed in this monograph may be perceived as homogeneous, global and general hybrids.