Abstract Evolutionary Ensemble Learning (EEL) provides a general approach for scaling
evolutionary learning algorithms to increasingly complex tasks. This is generally achieved …

Genetic Programming (GP) is an automated method for creating computer programs starting
from a high-level description of the problem to be solved. Many variants of GP have been …

Several general evolutionary approaches have proven quite successful at evolving teams
(or ensembles) consisting of cooperating team members. However, in this paper we …

Evolutionary algorithms are effective at creating cooperative, multi-agent systems. However,
current Island and Team algorithms show subtle but significant weaknesses when it comes …

Evolution of multi-agent teams has been shown to be an effective method of solving complex
problems involving the exploration of an unknown problem space. These autonomous and …

Research has shown that evolutionary algorithms are a promising approach for training
agents in heterogeneous multi-agent systems. However, research in evolving teams (or …

Evolution has proven to be an effective method of training heterogeneous multi-agent teams
of autonomous agents to explore unknown environments. Autonomous, heterogeneous …

There is considerable interest in develo** teams of autonomous, unmanned vehicles that
can function in hostile environments without endangering human lives. However …

This paper addresses the resolution, by Genetic Programming (GP) methods, of ambiguous
inverse problems, where for a single input, many outputs can be expected. We propose two …

AdaBoost is one of the most commonly used and most successful approaches for generating
ensemble classifiers. However, AdaBoost is limited in that it requires independent training …