… 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 …

… explain the required definitions in graph theory and genetic … IoT, (ie, devices connected to
the urban network). • For each … , paths in the initial conditions and traffic lights with fixed cycles). …

… 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, …

… This paper aims to propose an innovative model based on improved genetic programming
(… network has high prediction accuracy, but it is hard to explain the variables, and tends to over…

… 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 (…

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

… 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…

… 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]. …

… used in the domain of traffic signal control the current state of the … directed towards statistical
optimization of traffic signal … include fuzzy logic, neural networks and genetic algorithm (GA). …