The simultaneous optimization of multiple objectives arises in several problems in different disciplines. This optimization, mainly for many-objective problems brings challenges to the state-of-the-art Multi-Objective Evolutionary Algorithms. Given the ...

We construct and investigate a strongly embodied evolutionary system, where not only the controllers but also the morphologies undergo evolution in an on-line fashion. In these studies, we have been using various types of robot morphologies and ...

The application of evolutionary robotics [1] to swarm robotics gives evolutionary swarm robotics [8]. The evolution or learning of multi-agent behaviors is known to be challenging [7]. Hence, new approaches still need to be explored. Examples are ...

Based on previous results on language games here I study cultural dynamics extended in spatial environments. The underlying model makes assumptions regarding cognitive aspects of the individuals based on the Neuronal Replicator hypothesis. Although I ...

This paper aims to draw a comparison between the traditional view of hyper-heuristics and a lesser known type of multi-agent system known as a blackboard system. Both approaches share many similarities in both implementation and philosophy but also have ...

The behavior patterns in everyday life such as home, office, and commuting, and buying behavior model by day of the week, sea-son, location have hierarchies of various temporal granularity. Generally, in usual hierarchical data analysis, a basic ...

In this paper we show the potentiality of Embodied Evolution in the optimization of general multi-robot systems, as compared to state-of-the-art approaches based on Cooperative Coevolution. The comparison is carried out in a real application problem of ...

Social learning enables multiple robots to share learned experiences while completing a task. The literature offers examples where robots trained with social learning reach a higher performance compared to their individual learning counterparts [e.g, 2, ...

Implementing lifetime learning by means of on-line evolution, we establish an indirect encoding scheme that combines Compositional Pattern Producing Networks (CPPNs) and Central Pattern Generators (CPGs) as a relevant learner and controller for open-...

Recent advances in sampling-based Model Predictive Control (MPC) methods have enabled the control of nonlinear stochastic dynamical systems with complex and non-smooth cost functions. However, the main drawback of these methods is that they can be ...

Recently there has been increasing research attention focused on producing adaptive control systems for autonomous vehicles. To accommodate such autonomous vehicles there have been proposals that current road and highway infrastructure undergo ...

Previous research using evolutionary computation in Multi-Agent Systems indicates that assigning fitness based on team vs. individual behavior has a strong impact on the ability of evolved teams of artificial agents to exhibit teamwork in challenging ...

In many real-world situations in which resources are scarce, aligning the optimum of the system with the optimum of agents can be conflicting. For instance, in traffic assignment, the system's and the agents' welfare may not be aligned. In order to deal ...