Genetic Algorithm for the Weight Maximization Problem on Weighted Automata

The weight maximization problem (WMP) is the problem of finding the word of highest weight on a weighted finite state automaton (WFA). It is an essential question that emerges in many optimization problems in automata theory. Unfortunately, the general problem can be shown to be undecidable, whereas its bounded decisional version is NP-complete. Designing efficient algorithms that produce approximate solutions to the WMP in reasonable time is an appealing research direction that can lead to several new applications including formal verification of systems abstracted as WFAs. In particular, in combination with a recent procedure that translates a recurrent neural network into a weighted automaton, an algorithm for the WMP can be used to analyze and verify the network by exploiting the simpler and more compact automata model. In this work, we propose, implement and evaluate a metaheuristic based on genetic algorithms to approximate solutions to the WMP. We experimentally evaluate its performance on examples from the literature and show its potential on different applications.Comment: Accepted at GECCO 202

Ethical Issues in Open Source Software

In this essay we argue that the current social and ethical structure in the Open Source Software (OSS) Community stem from its roots in academia. The individual developers experience a level of autonomy similar to that of a faculty member. Furthermore, we assert that the Open Source Software Community\u27s social structure demands benevolent leadership. We argue that it is difficult to pass off low quality open source software as high quality software and that the Open Source development model offers strong accountability. Finally, we argue that Open Source Software introduces ethical challenges for universities and the software development community