A genetic algorithm using conventional heuristics as operators is considered in this study for the traveling salesman problem with backhauls (TSPB). Properties of a crossover operator (Nearest Neighbor Crossover, NNX) based on the nearest neighbor heuristic and the idea of using more than two parents are investigated in a series of experiments. Different parent selection and replacement strategies and generation of multiple children are tried as well. Conventional improvement heuristics are also used as mutation operators. It has been observed that 2-edge exchange and node insertion heuristics work well with NNX using only two parents. The best settings among different alternatives experimented are applied on traveling salesman problem with backhauls (TSPB). TSPB is a problem in which there are two groups of customers. The aim is to minimize the distance traveled visiting all the cities, where the second group can be visited only after all cities in the first group are already visited. The approach we propose shows very good performance on randomly generated TSPB instances.

While mutation testing is an effective fault-based testing technique, it has its challenges such as being computationally expensive and requiring a large amount of effort to review surviving mutants. These problems have resulted in mutation testing mostly being restricted to academic research and not as widely adopted in the industry. In the academic context, the focus has been on maximizing the mutation score and while a high mutation score might increase the quality of the software, it is not feasible to kill all the mutants. Moreover, all mutants are not as equally important, and some can not or should not be killed. Instead, the focus should be shifted to prioritizing the productive mutants, those that further improve the test suite or the source code. This thesis investigated if some mutation operators are more suitable for certain types of software by using selective mutation. The mutation operators were evaluated based on their ability to generate productive mutants. Moreover, the mutation operators were analyzed to identify how they could be improved to reduce the number of unproductive mutants generated by them. Dextool Mutate was used to conduct mutation testing on four open-source C/C++ software that were all different types of software. It was concluded that some mutation operators are more suitable for certain types of software resulting in the proposal of a set of mutation operators for each software type. Moreover, various improvements for the mutation operators were identified that reduce the number of unproductive mutants generated. Lastly, it may be helpful to customize the implementation of mutation operators for each type of software and some software types may require additional specialized mutation operators.

This thesis deals with analysis of genetic algorithms. It is focused on various approaches to creation of new populations. A comparison between basic principles of operation of genetic algorithms and processes occurring in living organisms is drawn here. Some methods of application of particular steps of genetic algorithms are introduced and a suitability of the methods to certain types of problems is considered. The main goal in the thesis is to apply genetic algorithms in solving three types of optimization problems, namely the solution of functions with a single major extreme, functions with flat (slight) extreme and also functions with many local extremes.