# SPL Features Quantification and Selection Based on Multiple Multi-Level Objectives
## 2 Literature Review
In Table 1, we summarize the core contributions and the methods used by the authors of the above articles:  
[PERSON] et al. [11] complementing the work of [PERSON] et al. [2] showed the superiority of IBEA over NSGA-2 and SPEA 2 in searching complex structures with many objectives (multiple objectives) through multiple graphs using five hours experiment using the hyper volume indicator, the spread and percentage of correct indicator. [PERSON] et al. [6] modified the original IBEA algorithm to SATIBEA based on MOO (Multi-Objective Optimization) with multiple objectives search-based optimization with constraints, for feature selection. It was based on five objectives: correctness, the richness of features, used before features, known defects, and cost. The work is mainly focused on handling constraints as the previous work of [PERSON] et al. [11] have not explicitly included any techniques. SATIBEA works by incorporating two smart search operators, smart mutation and smart replacement operators, along with constraints solving. [PERSON] et al. [1] complement the work of [PERSON] et al. [11] by accepting IBEA, which outclasses the generally-used MOEAs and adopts their fitness function in their experimentation for features selection mechanism. The tests performed using IVEA algorithm was compared with IBEA, NSGA 2, and SPEA2 algorithms. The results showed the supremacy of IVEA over the other on NFR Optimization in terms of execution time. [PERSON] et al. [12] improved the IBEA algorithm for MOEAs by adding Differential Evaluation (DE) as IBEA is believed to be underperforming in finding diverse and correct solutions. The combination proposed is to address the issue of product configuration in such a way that to find features that have zero conflicts and at the same time try to achieve the MOEAs by reducing the cost and increasing the number of features. IBED is a combination of IBEA and DE, which incorporates feedback mechanism for finding correct solutions. [PERSON] et al. [13] suggested a method called Product Portfolio Scoping (PPS), which is a marketing-based approach focusing on how features of a product should be developed by emphasizing on cost reduction and customer satisfaction by categorizing it as an NP-hard problem. They used a hybrid algorithm approach to combine NSGA 2 and Mamdani FUZZY inference system. The binary objectives tested in the approach were cost and relevance of features for each customer segment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \# & **Paper** & **Algorithm used** &
\begin{tabular}{c} **Algorithms used for** \\ **comparison/validation** \\ \end{tabular} & **Results** \\ \hline \multirow{4}{*}{1.} & A genetic algorithm for & & & Time consumption in larger \\  & optimized feature & & FCF + BBLP & data set GAFES has an \\  & selection with resource & & FCF + M-HEU & advantage while in small \\  & constraints in SPL & & & data set the competitors had \\ \hline \multirow{4}{*}{2.} & Ant colony & & & 6\% worse running time than \\  & optimization & & FCF + M-HEU & FCF+M-HEU \\  & feature selection in SPL & (GA-ACO & GAFES & 10\% better but taking more \\  & algorithm) & & & time \\ \hline \multirow{4}{*}{3.} & Automatic feature & & & Besides feature generation \\  & generation and & -FESPA & & and feature selection, Data \\ \cline{1-1}  & selection in predictive & -Decision tree & & cleaning is also an important \\ \cline{1-1}  & analytics solutions & & -Explore kit for & part of the data \\ \cline{1-1}  & analytics solutions & & feature selection & & put of the scope of this \\ \cline{1-1}  & & & & research. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithms for feature selection. GAFES: Genetic Algorithm for FEature Selection; FCF: Filtered Cartesian Flattering; BBLP: Branch and Bound with Linear Programming; M-HEU: Modified HEUristic. The validation of the work was done by asking the practitioners who were interviewed for the configuration of the fuzzy systems.