We explore data-driven methods for gaining insight into the dynamics of a two population genetic algorithm (GA), which has been effective for constrained optimization problems. We track and compare one population of feasible solutions and another population of infeasible solutions. Feasible solutions are selected and bred to improve their objective function values. Infeasible solutions are sele...

This work is concerned with a class of pde-constrained optimization problems that are motivated by an application in radiotherapy treatment planning. Here the primary design objective is to minimize the volume where a functional of the state violates a prescribed level, but prescribing these levels in the form of pointwise state constraints leads to infeasible problems. We therefore propose an ...

Nonlinear vehicle control allocation is achieved through distributing the task of vehicle control among individual tire forces, which are constrained to nonlinear saturation conditions. A highlevel sliding mode control with adaptive upper bounds is considered to assess the body yaw moment and lateral force for the vehicle motion. The proposed controller only requires the online adaptation of co...

We present Joogie, a tool that detects infeasible code in Java programs. Infeasible code is code that does not occur on feasible control-flow paths and thus has no feasible execution. Infeasible code comprises many errors detected by static analysis in modern IDEs such as guaranteed null-pointer dereference or unreachable code. Unlike existing techniques, Joogie identifies infeasible code by pr...

Counterexample-guided abstraction refinement (CEGAR) is a property-directed approach for the automatic construction of an abstract model for a given system. The approach learns information from infeasible error paths in order to refine the abstract model. We address the problem of selecting which information to learn from a given infeasible error path. In previous work, we presented a method th...

Conflict analysis for infeasible subproblems is one of the key ingredients in modern SAT solvers. In contrast, it is common practice for today’s mixed integer programming solvers to discard infeasible subproblems and the information they reveal. In this paper, we try to remedy this situation by generalizing SAT infeasibility analysis to mixed integer programming. We present heuristics for branc...

In [1], Eisenberg and Faltings proposed a breakout [2] based hybrid approach to detect infeasible subset (IS) in constraint satisfaction problems. In this paper, we adopted this algorithm as a preprocessing procedure to reduce the over-constrained constraint satisfaction problem. Then a three procedures hybrid approach is employed to detect the irreducible infeasible subset (IIS) of identified ...

Many real-world issues can be formulated as constrained optimization problems and solved using evolutionary algorithms with penalty functions. To effectively handle constraints, this study hybridizes a novel genetic algorithm with the rough set theory, called the rough penalty genetic algorithm (RPGA), with the aim to effectively achieve robust solutions and resolve constrained optimization pro...

In this paper, a new kind of deadlock-free scheduling method based on genetic algorithm and reachability analysis of timed SPR nets is proposed to solve the scheduling problems of job shop without buffers. Under the framework of timed Petri nets model, the scheduling problem can be described as finding a feasible transition firing sequence in the Petri nets model to avoid deadlock situations an...