Using Genetic Improvement and Code Transplants to Specialise a C++ Program to a Problem Class

Genetic Improvement (GI) is a form of Genetic Programming that improves an existing program. We use GI to evolve a faster version of a C++ program, a Boolean satisfiability (SAT) solver called MiniSAT, specialising it for a particular problem class, namely Combinatorial Interaction Testing (CIT), using automated code transplantation. Our GI-evolved solver achieves overall 17 percent improvement, making it comparable with average expert human performance. Additionally, this automatically evolved solver is faster than any of the human-improved solvers for the CIT problem

Lower Bound Founded Logic of Here-and-There

International audienceA distinguishing feature of Answer Set Programming is that all atoms belonging to a stable model must be founded. That is, an atom must not only be true but provably true. This can be made precise by means of the constructive logic of Here-and-There, whose equilibrium models correspond to stable models. One way of looking at foundedness is to regard Boolean truth values as ordered by letting true be greater than false. Then, each Boolean variable takes the smallest truth value that can be proven for it. This idea was generalized by Aziz to ordered domains and applied to constraint satisfaction problems. As before, the idea is that a, say integer, variable gets only assigned to the smallest integer that can be justified. In this paper, we present a logical reconstruction of Aziz’ idea in the setting of the logic of Here-and-There. More precisely, we start by defining the logic of Here-and-There with lower bound founded variables along with its equilibrium models and elaborate upon its formal properties. Finally, we compare our approach with related ones and sketch future work

A Methodology for Evaluating the Extensibility of Boolean Networks’ Structure and Function

International audienceFormal interaction networks are well suited for representing complex biological systems and have been used to model signalling pathways, gene regulatory networks, interaction within ecosystems, etc. In this paper, we introduce Sign Boolean Networks (SBNs), which are a uniform variant of Threshold Boolean Networks (TBFs). We continue the study of the complexity of SBNs and build a new framework for evaluating their ability to extend, i.e. the potential to gain new functions by addition of nodes, while also maintaining the original functions. We describe our software implementation of this framework and show some first results. These results seem to confirm the conjecture that networks of moderate complexity are the most able to grow, because they are not too simple, but also not too constrained, like the highly complex ones

Personalized course schedule planning using answer set programming

Course scheduling or timetabling is a well-known problem that is generally studied from the perspective of schools; the goal is to schedule the courses, considering, e.g., the expected number of students, the sizes of the available classrooms, time conflicts between courses of the same category. We study a complementary problem to help the students during the course registration periods; the goal is to plan personalized course schedules for students, considering, e.g., their preferences over sections, instructors, distribution of the courses. We present a declarative method to compute personalized course schedules, and an application of this method using answer set programming, and discuss promising results of some preliminary user evaluations via surveys