Bryan F. Jones

TL;DR: It is argued that software engineering is ideal for the application of metaheuristic search techniques, such as genetic algorithms, simulated annealing and tabu search, which could provide solutions to the difficult problems of balancing competing competing constraints.

TL;DR: Genetic algorithms have been used to generate test sets automatically by searching the domain of the software for suitable values to satisfy a predefined testing criterion, and have been applied successfully to several problems, varying in complexity from a quadratic equation solver to a generic sort module that comprises several procedures.

TL;DR: Metaheuristic techniques such as genetic algorithms, simulated annealing and tabu search have found wide application in most areas of engineering as discussed by the authors, however, they have not been more widely applied to software engineering.

TL;DR: Genetic algorithms are able to check large programs and they show considerable promise in establishing the validity of the temporal behaviour of real-time software.

TL;DR: The proposed method is the combination of a fuzzy multi-objective programming and a genetic algorithm for the optimization of fire station locations, which has three distinguish features: considering fuzzy nature of a decision maker (DM) in the location optimization model, being more understandable and practical to DM.