## 3 Research Methodology
### Study Selection and Quality Assessment  
We selected studies through these steps:  
1. Initial selection: We searched each database mentioned in Section 3.2. We first chose studies based on their titles.
2. Filtering studies: To find the most relevant studies from our initial collection, we used our quality assessment criteria. We also looked at the abstract, introduction, and conclusion of each study to filter them further.  
Figure 1: Literature distribution.  
3 Merging: After filtering, we had a group of studies relevant to our research. Some were duplicates because of overlaps in database results. We combined all the studies into one set, removing duplicates.
4. Snowballing: To find more related studies and make sure we didn't miss anything, we used backward and forward snowballing. Backward snowballing means looking at a study's reference list to find new papers [20]. Forward snowballing means finding new papers that cite the study we're looking at [20]. These processes helped us add new papers we didn't find in our first search.
5. Final Decision: After adding the new studies from snowballing, we filtered our set of studies one more time to get our final group of relevant studies. We applied a structured quality assessment using explicit inclusion and exclusion criteria to select the studies. The criteria were defined as follows:  
1. Inclusion Criteria: 1. 1.  
* Search Strategy: This criterion examines the techniques and approaches employed in the solutions presented by relevant studies. These may include, but are not limited to RL, EA, and other methodologies prevalent in the existing literature.
* Search Type: This criterion identifies if the search strategy searches for both generator and discriminator networks, or if it only searches for a generator network.
* Performance Assessment Strategy: This criterion examines how the search strategy estimates its current performance to guide the search.
* GPU Cost: This criterion identifies the search speed of the solutions presented by relevant studies based on its GPU usage.
* Advantages: This criterion examines the positive outcomes and potential merits associated with each methodological approach discussed in the existing literature.
* Disadvantages: This criterion identifies and analyzes any constraints, shortcomings, or negative aspects (where applicable) of the investigated approach.  
* Search Space: This criterion examines the type of search space used to encode the network component in the solutions presented by relevant studies. These may include, but are not limited to, Cell-based or Entire/Chain-Structured and other methodologies prevalent in the existing literature.  
* Evaluation Metrics: This criterion examines evaluation measurements used to evaluate the performance of the solution presented by the relevant studies.
* Dataset: This criterion identifies the datasets used in the solution presented by relevant studies.
* Supported Generation Type: This criterion identifies the types of generation tasks that are supported by the solution approaches. These may include, unconditional image generation, conditional images generation or both.  
The findings derived from the literature review were critically examined in Section 4, aligning with the established research questions and their corresponding comparative criteria.