The main aim of this thesis is to develop a Decision Support System (DSS) framework for innovation management. Determinants of innovation are the features that determine the innovation performance. For this reason, feature subset selection problem becomes an important issue. In order to construct the core of the DSS, we proposed two algorithms, which are Simulated Annealing and Genetic Algorithm. Determination of relevant features and prediction accuracy are the main objectives. Our proposed algorithms have been checked on two different data sets, Iris and Concrete Compressive Strength. After validation, algorithms have been implemented on innovation performance data. Feature weights that are obtained and prediction accuracies are presented for comparing and interpreting our algorithms
