diff --git a/alib2graph_algo/src/shortest_path/GreedyBestFS.cpp b/alib2graph_algo/src/shortest_path/GreedyBestFS.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..d30a54c1ab4d06dca01b3ee90cc12040fa4d5bcf
--- /dev/null
+++ b/alib2graph_algo/src/shortest_path/GreedyBestFS.cpp
@@ -0,0 +1,94 @@
+// GreedyBestFS.cpp
+//
+// Created on: 21. 02. 2018
+// Author: Jan Uhlik
+// Modified by:
+//
+// Copyright (c) 2017 Czech Technical University in Prague | Faculty of Information Technology. All rights reserved.
+// Git repository: https://gitlab.fit.cvut.cz/algorithms-library-toolkit/automata-library
+
+#include "GreedyBestFS.hpp"
+
+#include <registration/AlgoRegistration.hpp>
+
+namespace {
+
+// ---------------------------------------------------------------------------------------------------------------------
+
+auto GreedyBestFS1 = registration::AbstractRegister<shortest_path::GreedyBestFS,
+ ext::pair<ext::vector<DefaultNodeType>, DefaultWeightType>,
+ const graph::WeightedUndirectedGraph<> &,
+ const DefaultNodeType &,
+ const DefaultNodeType &,
+ std::function<DefaultWeightType(const DefaultNodeType &,
+ const DefaultNodeType &)> >(
+ shortest_path::GreedyBestFS::findPathRegistration);
+
+auto GreedyBestFS2 = registration::AbstractRegister<shortest_path::GreedyBestFS,
+ ext::pair<ext::vector<DefaultNodeType>, DefaultWeightType>,
+ const graph::WeightedUndirectedMultiGraph<> &,
+ const DefaultNodeType &,
+ const DefaultNodeType &,
+ std::function<DefaultWeightType(const DefaultNodeType &,
+ const DefaultNodeType &)> >(
+ shortest_path::GreedyBestFS::findPathRegistration);
+
+auto GreedyBestFS3 = registration::AbstractRegister<shortest_path::GreedyBestFS,
+ ext::pair<ext::vector<DefaultNodeType>, DefaultWeightType>,
+ const graph::WeightedDirectedGraph<> &,
+ const DefaultNodeType &,
+ const DefaultNodeType &,
+ std::function<DefaultWeightType(const DefaultNodeType &,
+ const DefaultNodeType &)> >(
+ shortest_path::GreedyBestFS::findPathRegistration);
+
+auto GreedyBestFS4 = registration::AbstractRegister<shortest_path::GreedyBestFS,
+ ext::pair<ext::vector<DefaultNodeType>, DefaultWeightType>,
+ const graph::WeightedDirectedMultiGraph<> &,
+ const DefaultNodeType &,
+ const DefaultNodeType &,
+ std::function<DefaultWeightType(const DefaultNodeType &,
+ const DefaultNodeType &)> >(
+ shortest_path::GreedyBestFS::findPathRegistration);
+
+auto GreedyBestFS5 = registration::AbstractRegister<shortest_path::GreedyBestFS,
+ ext::pair<ext::vector<DefaultNodeType>, DefaultWeightType>,
+ const graph::WeightedMixedGraph<> &,
+ const DefaultNodeType &,
+ const DefaultNodeType &,
+ std::function<DefaultWeightType(const DefaultNodeType &,
+ const DefaultNodeType &)> >(
+ shortest_path::GreedyBestFS::findPathRegistration);
+
+auto GreedyBestFS6 = registration::AbstractRegister<shortest_path::GreedyBestFS,
+ ext::pair<ext::vector<DefaultNodeType>, DefaultWeightType>,
+ const graph::WeightedMixedMultiGraph<> &,
+ const DefaultNodeType &,
+ const DefaultNodeType &,
+ std::function<DefaultWeightType(const DefaultNodeType &,
+ const DefaultNodeType &)> >(
+ shortest_path::GreedyBestFS::findPathRegistration);
+
+auto GreedyBestFSGrid1 = registration::AbstractRegister<shortest_path::GreedyBestFS,
+ ext::pair<ext::vector<DefaultSquareGridNodeType>,
+ DefaultWeightType>,
+ const grid::WeightedSquareGrid4<> &,
+ const DefaultSquareGridNodeType &,
+ const DefaultSquareGridNodeType &,
+ std::function<DefaultWeightType(const DefaultSquareGridNodeType &,
+ const DefaultSquareGridNodeType &)> >(
+ shortest_path::GreedyBestFS::findPathRegistration);
+
+auto GreedyBestFSGrid2 = registration::AbstractRegister<shortest_path::GreedyBestFS,
+ ext::pair<ext::vector<DefaultSquareGridNodeType>,
+ DefaultWeightType>,
+ const grid::WeightedSquareGrid8<> &,
+ const DefaultSquareGridNodeType &,
+ const DefaultSquareGridNodeType &,
+ std::function<DefaultWeightType(const DefaultSquareGridNodeType &,
+ const DefaultSquareGridNodeType &)> >(
+ shortest_path::GreedyBestFS::findPathRegistration);
+
+// ---------------------------------------------------------------------------------------------------------------------
+
+}
diff --git a/alib2graph_algo/src/shortest_path/GreedyBestFS.hpp b/alib2graph_algo/src/shortest_path/GreedyBestFS.hpp
new file mode 100644
index 0000000000000000000000000000000000000000..9f53bbbec02b2c3f0230676102e5f9839c9bcee1
--- /dev/null
+++ b/alib2graph_algo/src/shortest_path/GreedyBestFS.hpp
@@ -0,0 +1,194 @@
+// GreedyBestFS.hpp
+//
+// Created on: 21. 02. 2018
+// Author: Jan Uhlik
+// Modified by:
+//
+// Copyright (c) 2017 Czech Technical University in Prague | Faculty of Information Technology. All rights reserved.
+// Git repository: https://gitlab.fit.cvut.cz/algorithms-library-toolkit/automata-library
+
+#ifndef ALIB2_GREEDYBESTFS_HPP
+#define ALIB2_GREEDYBESTFS_HPP
+
+#include <functional>
+#include <alib/vector>
+#include <alib/pair>
+#include <alib/set>
+#include <alib/map>
+
+#include <common/ReconstructPath.hpp>
+#include <common/SupportFunction.hpp>
+
+namespace shortest_path {
+
+class GreedyBestFS {
+// ---------------------------------------------------------------------------------------------------------------------
+
+ public:
+
+ /// Find the shortest path using Greedy Best-first search algorithm from the \p start node to the \p goal node in the \p graph.
+ /// \note The founded path is not necessary the optimal one.
+ ///
+ /// Whenever node is opened, \p f_user is called with two parameters (the opened node and value of currently shortest path).
+ ///
+ /// \param graph to explore.
+ /// \param start initial node.
+ /// \param goal final node.
+ /// \param f_heuristic heuristic function which accept node and return edge_type::weight_type.
+ /// \param f_user function which is called for every opened node with value of currently shortest path.
+ ///
+ /// \returns pair where first := shortest path := distance of path, if there is no such path vector is empty and distance std::numeric_limits<edge_type:weight_type>::max().
+ ///
+ /// \note TEdge of \p graph must follow graph::edge::WeightedEdge interface.
+ /// \sa graph::edge_type::WeightedEdge.
+ ///
+ /// \throws std::out_of_range if \p graph contains an edge with a negative weight.
+ ///
+ template<
+ typename TGraph,
+ typename TNode,
+ typename F1 = std::function<typename TGraph::edge_type::weight_type(const TNode &)>,
+ typename F2 = std::function<void(const TNode &, const typename TGraph::edge_type::weight_type &)>>
+ static
+ ext::pair<ext::vector<TNode>, typename TGraph::edge_type::weight_type>
+ findPath(const TGraph &graph,
+ const TNode &start,
+ const TNode &goal,
+ F1 f_heuristic,
+ F2 f_user = [](const TNode &,
+ const typename TGraph::edge_type::weight_type &) {});
+
+ template<
+ typename TGraph,
+ typename TNode,
+ typename F1 = std::function<typename TGraph::edge_type::weight_type(const TNode &, const TNode &)>
+ >
+ static
+ ext::pair<ext::vector<TNode>, typename TGraph::edge_type::weight_type>
+ findPathRegistration(const TGraph &graph,
+ const TNode &start,
+ const TNode &goal,
+ F1 f_heuristic) {
+ return findPath(graph, start, goal, [&](const TNode &n) { return f_heuristic(goal, n); });
+ }
+
+// =====================================================================================================================
+
+ private:
+
+ template<typename TNode, typename TWeight>
+ struct Data {
+ ext::set<ext::pair<TWeight, TNode>> queue; // priority queue
+ ext::map<TNode, TWeight> g; // G score
+ ext::map<TNode, TNode> p; // parents
+ };
+
+// ---------------------------------------------------------------------------------------------------------------------
+
+ template<typename FEdges, typename TNode, typename TWeight, typename F1, typename F2, typename F3>
+ static bool relaxation(FEdges successor_edges,
+ Data<TNode, TWeight> &data,
+ F1 f_heuristic,
+ F2 f_user,
+ F3 f_stop);
+
+// ---------------------------------------------------------------------------------------------------------------------
+
+ template<typename TNode, typename TWeight, typename F>
+ inline static void init(GreedyBestFS::Data<TNode, TWeight> &data, const TNode &start, F f_heuristic);
+
+// ---------------------------------------------------------------------------------------------------------------------
+
+};
+
+// =====================================================================================================================
+
+template<typename TGraph, typename TNode, typename F1, typename F2>
+ext::pair<ext::vector<TNode>, typename TGraph::edge_type::weight_type>
+GreedyBestFS::findPath(const TGraph &graph,
+ const TNode &start,
+ const TNode &goal,
+ F1 f_heuristic,
+ F2 f_user) {
+ using weight_type = typename TGraph::edge_type::weight_type;
+
+ Data<TNode, weight_type> data;
+
+ // Init search
+ init(data, start, f_heuristic);
+
+ while (!data.queue.empty()) {
+ bool stop = relaxation([&](const auto &node) -> auto { return graph.successorEdges(node); },
+ data,
+ f_heuristic,
+ f_user,
+ [&goal](const TNode &n) -> bool { return goal == n; });
+
+ if (stop) {
+ break;
+ }
+ }
+
+ return common::ReconstructPath::reconstructWeightedPath(data.p, data.g, start, goal);
+}
+
+// ---------------------------------------------------------------------------------------------------------------------
+
+template<typename FEdges, typename TNode, typename TWeight, typename F1, typename F2, typename F3>
+bool
+GreedyBestFS::relaxation(FEdges successor_edges,
+ GreedyBestFS::Data<TNode, TWeight> &data,
+ F1 f_heuristic,
+ F2 f_user,
+ F3 f_stop) {
+ TNode n = data.queue.begin()->second;
+ data.queue.erase(data.queue.begin());
+
+ // Run user's function
+ f_user(n, data.g[n]);
+
+ // Stop if reach the goal
+ if (f_stop(n)) {
+ return true;
+ }
+
+ for (const auto &s_edge: successor_edges(n)) {
+ const TNode &s = common::SupportFunction::other(s_edge, n); // successor
+
+ // Check for negative edge
+ if (s_edge.weight() < 0) {
+ throw std::out_of_range("GreedyBestFS: Detect negative weight on edge in graph.");
+ }
+
+ // Calculate new G score
+ TWeight gscore = data.g.at(n) + s_edge.weight();
+
+ // Search if the node s was already visited
+ auto search_g = data.g.find(s);
+
+ // If not or the G score can be improve do relaxation
+ if (search_g == data.g.end() || data.g.at(s) > gscore) {
+ data.g[s] = gscore;
+ data.p.insert_or_assign(s, n);
+ if (search_g == data.g.end()) {
+ data.queue.insert(ext::make_pair(f_heuristic(s), s));
+ }
+ }
+ }
+
+ return false;
+}
+
+// ---------------------------------------------------------------------------------------------------------------------
+
+template<typename TNode, typename TWeight, typename F>
+void GreedyBestFS::init(GreedyBestFS::Data<TNode, TWeight> &data, const TNode &start, F f_heuristic) {
+ data.g[start] = 0;
+ data.p.insert_or_assign(start, start);
+ data.queue.insert(ext::make_pair(f_heuristic(start), start));
+}
+
+// ---------------------------------------------------------------------------------------------------------------------
+
+} // namespace shortest_path
+#endif //ALIB2_GREEDYBESTFS_HPP