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This work presents how agent hierarchical aggrega- tions can solve real-world classification problems. The classification problems we are interested in, ...
The importance of selecting relevant features for data modeling has been recognized already in machine learning. This paper discusses the application of an ...
Evolving agent aggregates using cellular genetic algorithms. Authors: Xavier ... Evolving agent aggregates using cellular genetic algorithms. Page 868 ...
Evolving multi-agents using a self-organizing genetic algorithm
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This paper introduces the use of a genetic algorithm to discover rules that govern emergent cooperative behavior. A self-organizing genetic algorithm was ...
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Evolutionary algorithms (EAs) and the multi-agent systems (MAS) are two independent, well-known problem-solving paradigms with different characteristics ...
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ABSTRACT. Some problems can be solved only by multi–agent teams. In using genetic programming to produce such teams, one faces several design decisions.
also outline a number of open questions and future directions for genetic algorithms in artificial-life research. 1 Introduction. Evolution by natural selection ...
Oct 22, 2024 · The idea is to capture in the fitness function the goal of the design process (efficiency for MAS that realize a given function, realism for ...
Jul 10, 2024 · We define a fitness function that rewards agents for both capabilities and use the OpenAI-ES algorithm to evolve such behavior. Our outcomes.
Feb 20, 2024 · The method consists of multi-agent reinforcement learning, Graph Neural Networks (GNNs), and genetic algorithms. The ERLGA [50] method discusses ...