Explainable artificial intelligence (XAI) has received great interest in the recent decade, due
to its importance in critical application domains, such as self-driving cars, law, and …

“Exploration and exploitation are the two cornerstones of problem solving by search.” For
more than a decade, Eiben and Schippers' advocacy for balancing between these two …

Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic …

We investigate the effects of semantically-based crossover operators in genetic
programming, applied to real-valued symbolic regression problems. We propose two new …

All metaheuristic optimization algorithms require some initialization, and the initialization for
such optimizers is usually carried out randomly. However, initialization can have some …

Several methods to incorporate semantic awareness in genetic programming have been
proposed in the last few years. These methods cover fundamental parts of the evolutionary …

The field of evolutionary computation (EC) can no longer be considered an esoteric one.
Today, after about thirty years of research, a rich corpus of theory exists and many …

Dynamic flexible job shop scheduling is a prominent combinatorial optimisation problem
with many real-world applications. Genetic programming has been widely used to …

Besides the difficulty of the application problem to be solved with Genetic Algorithms (GAs),
an additional difficulty arises because the quality of the solution found, or the computational …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …