  Computing diverse solutions for a given problem, in particular evolutionary
diversity optimisation (EDO), is a hot research topic in the evolutionary
computation community. This paper studies the Boolean satisfiability problem
(SAT) in the context of EDO. SAT is of great importance in computer science and
differs from the other problems studied in EDO literature, such as KP and TSP.
SAT is heavily constrained, and the conventional evolutionary operators are
inefficient in generating SAT solutions. Our approach avails of the following
characteristics of SAT: 1) the possibility of adding more constraints (clauses)
to the problem to forbid solutions or to fix variables, and 2) powerful solvers
in the literature, such as minisat. We utilise such a solver to construct a
diverse set of solutions. Moreover, maximising diversity provides us with
invaluable information about the solution space of a given SAT problem, such as
how large the feasible region is. In this study, we introduce evolutionary
algorithms (EAs) employing a well-known SAT solver to maximise diversity among
a set of SAT solutions explicitly. The experimental investigations indicate the
introduced algorithms' capability to maximise diversity among the SAT
solutions.
