Enhanced Metaheuristic Algorithms for the Identification of Cancer MDPs

METAHEURISTIC ALGORITHMS FOR MINIMUM CROSSING NUMBER PROBLEM

This paper presents the application of metaheuristic methods to the minimum crossing number problem for the first time. These algorithms including particle swarm optimization, improved ray optimization, colliding bodies optimization and enhanced colliding bodies optimization. For each method, a pseudo code is provided. The crossing number problem is NP-hard and has important applications in eng...

Enhanced ACS algorithms for plastic analysis of planar frames

In recent years, the trend in solving optimization problems has been directed toward using heuristic algorithms such as neural networks, genetic and ant colony algorithms. The main reason for this trend can be attributed to the fact that these algorithms can be efficiently adjusted to the specific search space to which they are applied and consequently they can be used for many optimization pro...

Reinforcement Learning Algorithms for MDPs

This article presents a survey of reinforcement learning algorithms for Markov Decision Processes (MDP). In the first half of the article, the problem of value estimation is considered. Here we start by describing the idea of bootstrapping and temporal difference learning. Next, we compare incremental and batch algorithmic variants and discuss the impact of the choice of the function approximat...

Anytime Algorithms for Solving Possibilistic MDPs and Hybrid MDPs

The ability of an agent to make quick, rational decisions in an uncertain environment is paramount for its applicability in realistic settings. Markov Decision Processes (MDP) provide such a framework, but can only model uncertainty that can be expressed as probabilities. Possibilistic counterparts of MDPs allow to model imprecise beliefs, yet they cannot accurately represent probabilistic sour...

Anytime Algorithms for Solving Possibilistic MDPs and Hybrid MDPs