Constraint programming solvers provide a generalizable approach to finding solutions for optimization problems. However, when comparing the performance of constraint programming solvers to the performance of a heuristic solver for an optimization problem such as cluster editing, the heuristic solver is able to find near-optimal and optimal solutions much faster. The goal of this research is to understand how the behaviour of such a heuristic solver can assist the performance of constraint programming solvers in optimization problems. In order to achieve this, first Chuffed, a state-of-the-art constraint programming solver was combined with a heuristic approach to cluster editing, with the goal of emulating the performance of the heuristic algorithm, in particular, being able to find near-optimal solutions faster. Continuing, the goal was to generalize the behaviour observed by the modified solver, by emulating the performance observed without the need for the specialized heuristic solver. The generalized approach is tested on a wide variety of different tests. An approach to value selection was developed that performs lookahead propagations for the two values of a boolean variable and selects the value that has the most optimal solution within the domain after performing the lookahead propagation. This approach added a significant time overhead that increased the overall solving time for many problems, with the lookahead configuration having a median increase of 8.7% over the default configuration for the optimization problems of the MiniZinc Challenge 2022. However, it was able to successfully emulate the performance of the heuristic solver, finding near-optimal solutions significantly faster than the default value selection. In particular, for the optimization problems of the MiniZinc Challenge 2022, on average, the lookahead configuration had a definite integral for the time vs objective graph 54.7% lower than the default Chuffed configuration.

Cluster editing attempts to find the minimum number of edge additions and removals on an undirected graph, that will transform the graph to one consisting of only disconnected cliques. In this paper, we propose three heuristic approaches to this problem, based on algorithms used to solve different clustering problems. The algorithms were based on agglomerative, divisive and k-means clustering algorithms. Experimental results show that all three algorithms are able to find results close to the minimum number of edits, but in particular, the k-means algorithm has a lower time complexity compared to the other two algorithms, while producing on average, the lowest number of edits.