Different researchers hold different views of what the term meta-learning exactlymeans. The
first part of this paper provides our own perspective view in which the goal isto build self …

If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These
rule‐based, multifaceted, machine learning algorithms originated and have evolved in the …

In classification, feature selection is an important data pre-processing technique, but it is a
difficult problem due mainly to the large search space. Particle swarm optimisation (PSO) is …

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems
comprised of autonomous, interacting agents. ABMS promises to have far reaching effects …

This e-book is devoted to global optimization algorithms, which are methods to find optimal
solutions for given problems. It especially focuses on Evolutionary Computation by …

In the past decade, Explainable Artificial Intelligence (XAI) has attracted a great interest in
the research community, motivated by the need for explanations in critical AI applications …

Role-based access control (RBAC) in databases provides a valuable level of abstraction to
promote security administration at the business enterprise level. With the capacity for …

Unbalanced classification is an essential machine learning task, which has attracted
widespread attention from both the academic and industrial communities due mainly to its …

Sentiment analysis of public views and spam detection from social media text messages are
two challenging data analysis tasks due to short informal text. This paper investigates the …

This paper introduces concepts associated with complex adaptive systems (cas), linking
those concepts at some points to economic planning. The paper begins (section 1) with an …