Co-evolution is a powerful problem-solving approach. However, fitness evaluation in co-evolutionary algorithms can be computationally expensive, as the quality of an individual in one population is often determined by its interactions with many (or all) ...

Containers are a popular way of deploying software in cloud data centers. Containers are allocated to Virtual machines (VMs) which are allocated to Physical machines (PMs) within the data center. Since the resources required by containers often do not ...

Numerous forms of consumer-generated media (CGM), such as social networking services (SNS), are widely used. Their success relies on users' voluntary participation, often driven by psychological rewards like recognition and connection from reactions by ...

When a language evolves, meta-models and associated gram- mars need to be co-evolved to stay mutually consistent. Previous work has supported the automated migration of a grammar after changes of the meta-model to retain manual optimizations of the ...

Live stream platforms have gained popularity in light of emerging social media platforms. Unlike traditional on-demand video platforms, viewers and streamers on the live stream platforms are able to interact in real-time, and this makes viewer interests ...

Generative Adversarial Networks (GANs) have gained popularity due to their ability to produce realistic examples from existing data without any supervision. However, they are dependent on their hyperparameters, the tuning of which is usually a manual ...

While competitive coevolutionary algorithms are ideally suited to model adversarial dynamics, their complexity makes it difficult to understand what is happening when they execute. To achieve better clarity, we introduce a game named DefendIt and ...

This paper's research scope is interactive evolutionary multiple objective optimization founded on the preference learning paradigm. It concerns a scenario in which the Decision Maker's (DM's) aspirations develop over time. In this view, the ...

Adaptation between users and computers is difficult because of the reciprocal long-term adaptation between the user and an adaptive tool. In this work in progress, we present a novel method for designing adaptive systems, by simulating reciprocal ...

Cooperation is an important challenge in multi-agent systems. Decentralised learning of cooperation is difficult because interactions between agents make the environment non-stationary, and the reward structure tempts agents to act selfishly. A ...

The importance of explainability in AI has become a pressing concern, for which several explainable AI (XAI) approaches have been recently proposed. However, most of the available XAI techniques are post-hoc methods, which however may be only partially ...

Minimal criterion co-evolution (MCC) is an evolutionary algorithm that uses a simple reproduction constraint between two interacting populations to drive an open-ended search process. While it has previously been applied to parameterise simple agents and ...

Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs. However, these algorithms are poorly understood and applications are often limited by ...

Cooperative Co-evolutionary Algorithms effectively train policies in multiagent systems with a single, statically defined team. However, many real-world problems, such as search and rescue, require agents to operate in multiple teams. When the structure ...

Designing optimal soft modular robots is difficult, due to non-trivial interactions between morphology and controller. Evolutionary algorithms (EAs), combined with physical simulators, represent a valid tool to overcome this issue. In this work, we ...

Model-driven engineering advocates the use of different modelling languages and multiple views to describe the characteristics of a complex system. This allows to express a specific system characteristic with the most appropriate modelling language. ...

Model-driven engineering advocates the use of different modelling languages and multiple views to describe the characteristics of a complex system. This allows to express a specific system characteristic with the most appropriate modelling language. ...

Co-evolution of metamodels and conforming models is a known challenge in model-driven engineering. A variation of co-evolution occurs in model-based software product line engineering, where it is needed to efficiently co-evolve various products together ...

Rule-based machine learning (RBML) algorithms such as learning classifier systems (LCS) are well suited to classification problems with complex interactions and heterogeneous associations. Alternatively, genetic programming (GP) has a complementary set ...

It had been noticed that, while coevolutionary computational systems have only a single objective when evaluating, there is a subtle multi-objective aspect to evaluation since different pairings can be thought of as different objectives (all in support ...