Autonomous vehicle navigation in an unknown dynamic environment is crucial for both supervised- and Reinforcement Learning-based autonomous maneuvering. The cooperative fusion of these two learning approaches has the potential to be an effective mechanism to tackle indefinite environmental dynamics. Most of the state-of-the-art autonomous vehicle navigation systems are trained on a specific mapped model with familiar environmental dynamics. However, this research focuses on the cooperative fusion of supervised and Reinforcement Learning technologies for autonomous navigation of land vehicles in a dynamic and unknown environment. The Faster R-CNN, a supervised learning approach, identifies the ambient environmental obstacles for untroubled maneuver of the autonomous vehicle. Whereas, the training policies of Double Deep Q-Learning, a Reinforcement Learning approach, enable the autonomous agent to learn effective navigation decisions form the dynamic environment. The proposed model is primarily tested in a gaming environment similar to the real-world. It exhibits the overall efficiency and effectiveness in the maneuver of autonomous land vehicles.

Evolutionary algorithms have a history of being applied into clustering analysis. However, most of the existing evolutionary clustering techniques fail to detect complex/spiral-shaped clusters. In our previous works, we proposed several evolutionary multi-objective clustering algorithms and achieved promising results. Still, they suffer from this usual problem exhibited by evolutionary and unsupervised clustering approaches. In this paper, we proposed an improved multi-objective evolutionary clustering approach (EMCOC) to resolve the overlapping problems in complex shape data. Experimental results based on several artificial and real-world data show that the proposed EMCOC can successfully identify overlapping clusters. It also succeeds obtaining non-dominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance. The superiority of the EMCOC over some other multi-objective evolutionary clustering algorithms is also confirmed by the experimental results.

In this paper, we present a novel multi-objective evolutionary clustering approach using variable-length real jumping genes genetic algorithms (VRJGGA). The proposed algorithm that extends jumping genes genetic algorithm (JGGA) (Man et al., 2004) evolves near-optimal clustering solutions using multiple clustering criteria, without a-priori knowledge of the actual number of clusters. Experimental results based on several artificial and real-world data show that VRJGGA can obtain non-dominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance