Evolutionary algorithms (EAs) have been applied with success to many numerical and combinatorial optimization problems in recent years. However, they often lose their effectivenes...
The TORCS Endurance World Championship is an international competition in which programmers develop and tune their drivers to race against each other using TORCS, a state-of-the-ar...
Luigi Cardamone, Daniele Loiacono, Pier Luca Lanzi
This paper is about the evolutionary design of multi-agent systems. An important part of recent research in this domain has been focusing on collaborative revolutionary methods. W...
In concurrent cooperative multiagent learning, each agent simultaneously learns to improve the overall performance of the team, with no direct control over the actions chosen by i...
In this paper, we propose a multilevel cooperative coevolution (MLCC) framework for large scale optimization problems. The motivation is to improve our previous work on grouping ba...
Abstract. Cooperative Coevolution is a technique in the area of Evolutionary Computation. It has been applied to many combinatorial problems with great success. This contribution p...
Cooperative coevolution has proven to be a promising technique for solving complex combinatorial optimization problems. In this paper, we present four different strategies which i...
Leonardo Vanneschi, Giancarlo Mauri, Andrea Valsec...
Cooperative coevolution is often used to solve difficult optimization problems by means of problem decomposition. Its performance on this task is influenced by many design decisio...
In previous work, we showed how cooperative coevolution could be used to evolve both the feature construction stage and the classification stage of an object detection algorithm. ...