… to be optimized during the learning process towards reaching an optimal policy π ∗ , the
agent takes action a k from a set of possible actions A in response to the current state s k …

… The goal of this chapter is to explain how the concepts of evolution and Epigenetics have …
order to escape from the tendency of the organism components towards entropy [91]. …

Signalized intersections are managed by controllers that assign right of way (green, yellow,
and red lights) to non-conflicting directions. Optimizing the actuation policy of such …

… This model works via a fuzzy genetic algorithm [29]. Although fuzzy … In this section, we use
an example to explain the reward R calculation in detail. Assume that the current state is s = [2, …

… take into account instances with more than 950 traffic lights and … Early attempts were mostly
based on Genetic Algorithms (… The proposed algorithm was evaluated on an urban network …

… for investment in variable traffic signs [1]. Previous research on … ] presented a methodology
using genetic algorithms and micro-… lanes than towards an SO traffic assignment (scenario C). …

… The genetic programming (GP) is a flexible tool different from the traditional evolutionary
computation (EC) [24], [25], and can produce computer programmes utilizing various operators (…

… signal times for urban intersections with a genetic algorithm … algorithm according to traffic
information obtained from field [34… traffic conditions and intersection structures, will be explained…

… framework employed—eg, neural network, genetic algorithm. … explainability from the
perspective of XAI. The prime hindrance towards developing the ground knowledge of explainability …

… In this work, a traffic light controller was designed using model predictive … accumulation and
trip completion rate for urban networks. For … and neural networks in a genetic algorithm [34]. …