An optimal recombination operator for two-parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions. Exploring this hyperplane… Continue reading Dynastic Potential Crossover Operator
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Real-World Data Science Projects Management
Real-World Data Science Projects involve the practical application of data science methodologies to solve real-world problems. These projects require interdisciplinary collaboration, deal with large and complex datasets, and encompass the entire project lifecycle from data collection to deployment. Ethical considerations and advanced analytics techniques are also key aspects of these projects.
Gray Box Optimization
In Gray Box Optimization, the optimizer is given access to the set of M subfunctions. We prove Gray Box Optimization can efficiently compute hyperplane averages to solve non-deceptive problems in time. Bounded separable problems are also solved in time. As a result, Gray Box Optimization is able to solve many commonly used problems from the… Continue reading Gray Box Optimization
Real-like MAX-SAT Instances and the Landscape Structure Across the Phase Transition
In contrast with random uniform instances, industrial SAT instances of large size are solvable today by state-of-the-art algorithms. It is believed that this is the consequence of the non-random structure of the distribution of variables into clauses. In order to produce benchmark instances resembling those of real-world formulas with a given structure, generative models have… Continue reading Real-like MAX-SAT Instances and the Landscape Structure Across the Phase Transition
AI Studies Survey
AI is a rapidly emerging field that has opened up new vistas of innovation and creativity. From intelligent systems to self-driving cars, AI has transformed the way we live and work. While AI is often studied as a subfield of computer science, it has grown so rapidly that it now encompasses many other fields. The… Continue reading AI Studies Survey
The Silent (R)evolution of SAT
THE PROPOSITIONAL SATISFIABILITY problem (SAT) was the first to be shown NP-complete by Cook and Levin. SAT remained the embodiment of theoretical worst-case hardness. However, in stark contrast to its theoretical hardness, SAT has emerged as a central target problem for efficiently solving a wide variety of computational problems. SAT solving technology has continuously advanced since a breakthrough around the millennium, which catapulted practical SAT solving ahead by orders of magnitudes. Today, the many flavors of SAT technology can be found in all areas of technological innovation.