You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review of Computational Methods for Clustering Genes with Similar Biological Functions 
Chapter Title:  Aim 
1. Abstract of Cited Paper (BIB001):  In this paper, we investigate how to apply the hybrid genetic algorithms (the memetic algorithms) to solve the parallel machine scheduling problem. There are two essential issues to be dealt with for all kinds of parallel machine scheduling problems: job partition among machines and job sequence within each machine. The basic idea of the proposed method is that (a) use the genetic algorithms to evolve the job partition and then (b) apply a local optimizer to adjust the job permutation to push each chromosome climb to his local optima. Preliminary computational experiments demonstrate that the hybrid genetic algorithm outperforms the genetic algorithms and the conventional heuristics. 
2. Abstract of Cited Paper (BIB002):  BackgroundPrevious studies on bladder cancer have shown nodal involvement to be an independent indicator of prognosis and survival. This study aimed at developing an objective method for detection of nodal metastasis from molecular profiles of primary urothelial carcinoma tissues.MethodsThe study included primary bladder tumor tissues from 60 patients across different stages and 5 control tissues of normal urothelium. The entire cohort was divided into training and validation sets comprised of node positive and node negative subjects. Quantitative expression profiling was performed for a panel of 70 genes using standardized competitive RT-PCR and the expression values of the training set samples were run through an iterative machine learning process called genetic programming that employed an N-fold cross validation technique to generate classifier rules of limited complexity. These were then used in a voting algorithm to classify the validation set samples into those associated with or without nodal metastasis.ResultsThe generated classifier rules using 70 genes demonstrated 81% accuracy on the validation set when compared to the pathological nodal status. The rules showed a strong predilection for ICAM1, MAP2K6 and KDR resulting in gene expression motifs that cumulatively suggested a pattern ICAM1>MAP2K6>KDR for node positive cases. Additionally, the motifs showed CDK8 to be lower relative to ICAM1, and ANXA5 to be relatively high by itself in node positive tumors. Rules generated using only ICAM1, MAP2K6 and KDR were comparably robust, with a single representative rule producing an accuracy of 90% when used by itself on the validation set, suggesting a crucial role for these genes in nodal metastasis.ConclusionOur study demonstrates the use of standardized quantitative gene expression values from primary bladder tumor tissues as inputs in a genetic programming system to generate classifier rules for determining the nodal status. Our method also suggests the involvement of ICAM1, MAP2K6, KDR, CDK8 and ANXA5 in unique mathematical combinations in the progression towards nodal positivity. Further studies are needed to identify more class-specific signatures and confirm the role of these genes in the evolution of nodal metastasis in bladder cancer. 
3. Abstract of Cited Paper (BIB003):  This study presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to modeling stage–discharge relationship. The results obtained are compared to more conventional methods, stage rating curve and multiple linear regression techniques. Statistical measures such as average, standard deviation, minimum and maximum values, as well as criteria such as mean square error and determination coefficient, the coefficient of efficiency, and the adjusted coefficient of efficiency are used to measure the performance of the models developed by employing GEP. Also, the explicit formulations of the developed GEP models are presented. Statistics and scatter plots indicate that the proposed equations produce quite satisfactory results and perform superior to conventional models. 
4. Abstract of Cited Paper (BIB004):  Choosing a small subset of genes that enables a good classification of diseases on the basis of microarray data is a difficult optimization problem. This paper presents a memetic algorithm, called MAGS, to deal with gene selection for supervised classification of microarray data. MAGS is based on an embedded approach for attribute selection where a classifier tightly interacts with the selection process. The strength of MAGS relies on the synergy created by combining a problem specific crossover operator and a dedicated local search procedure, both being guided by relevant information from a SVM classifier. Computational experiments on 8 well-known microarray datasets show that our memetic algorithm is very competitive compared with some recently published studies. 
5. Abstract of Cited Paper (BIB005):  In this study, a new metaheuristic optimization algorithm, called cuckoo search (CS), is introduced for solving structural optimization tasks. The new CS algorithm in combination with Levy flights is first verified using a benchmark nonlinear constrained optimization problem. For the validation against structural engineering optimization problems, CS is subsequently applied to 13 design problems reported in the specialized literature. The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area. The optimal solutions obtained by CS are mostly far better than the best solutions obtained by the existing methods. The unique search features used in CS and the implications for future research are finally discussed in detail. 
6. Abstract of Cited Paper (BIB006):  Artificial bee colony (ABC) has proved to be a good optimizer on many engineering problems. However, the standard ABC converges slowly because of poor exploitation. Recently, a new ABC variant called global best guided ABC (namely GABC) was proposed. Some simulation studies proved that GABC could find more accurate solutions than the standard ABC. In this paper, an improved ABC (namely IABC) is proposed to strengthen the performance of GABC. In IABC, the onlooker bees use a modified solution search equation to improve the exploitation capability. Experiment verification is carried out on ten classical benchmark functions. Results show our new approach IABC is superior to the standard ABC, GABC and another improved ABC variant. 
7. Abstract of Cited Paper (BIB007):  Abstract Abandoned areas with substances potentially harmful to the environment or human health have raised numerous concerns around the world. The objective of the present study is to analyze the possibility of building solar power plants capable of capturing solar energy in unproductive areas, both contaminated and uncontaminated. For this purpose, American data from the National Solar Radiation Database (NSRDB) were used, a collection of hourly solar radiation measurements and meteorological data, as well as data from the RE-Powering America's Land project, run by the United States Environmental Protection Agency (EPA). In the analysis, the information about “mapped area”, “distances to the transmission lines”, “solar direct normal irradiance on a utility scale” and “off-grid direct normal irradiance” were considered. To define the best locations, the data were initially pre-processed. A new hybrid fuzzy c-means (HFCM) algorithm was then applied, initialized, comparatively, by three metaheuristics: Differential Evolution (DE), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), for the data clustering. The number of clusters obtained was validated by three metrics: Calinski-Harabasz Criterion, Davies-Bouldin Criterion and Silhouette Coefficient, with all three unanimously indicating two clusters as the ideal number: one cluster for locations with greater potential for allocating facilities to capture solar energy, and another for locations with a lower potential. With the new approach, an increase of 23.3% in the training velocity of the HFCM algorithm was identified, which required fewer iterations to achieve the same value of the objective function. Else, a round of experiments was conducted with six different datasets (instances from literature) and the results showed that the proposed method can achieve better results (faster convergence and smaller solution cost) than the classic FCM. Visually, the predominance of the allocation of facilities was perceived in states with a greater average incidence of solar radiation. Therefore, this was the predominant factor in the convergence of the algorithm, which is in accordance with expectations for solar energy. Finally, the social, economic and environmental gains were considered with the revitalization of unproductive land with the possibility of implementing solar power plants in these areas. 
8. Abstract of Cited Paper (BIB008):  Cluster analysis is a data mining technique that has been widely used to exploit useful information in a great amount of data. Because of their evaluation mechanism based on an intracluster distance (ICD) function, traditional single-objective clustering algorithms are not appropriate for not-well-separated data. Specifically, they may easily result in the drop of the optimal solution accuracy on their late stages of search when dealing with the latter. To overcome the problem, in this paper a novel index reflecting the similarity of data within a cluster is presented and called intracluster cohesion (ICC). However, if a multiobjective method is used to cluster with ICD and ICC as the specified objectives, its clustering accuracy may depend on one's experience. Motivated by these, we propose an accelerated two-stage particle swarm optimization (ATPSO) in which K-means is utilized to accelerate particles' convergence during the population initialization. Its clustering process consists of two stages. First, the main objective of minimizing ICD is to execute preliminary clustering; second, ICC is optimized to promote the clustering accuracy. Extensive experiments with the help of 17 open-source clustering sets in various geometric distributions are conducted. The results show that ATPSO outperforms PSO, K-means PSO (KPSO), chaotic PSO (CPSO), and accelerated CPSO in terms of accuracy, and its efficiency is approximate to that of KPSO. Its convergence trend indicates that the adoption of the proposed ICC contributes to the clustering accuracy. Remarkably, compared with the Pareto-based multiobjective PSO, ATPSO can detect clusters more accurately and quickly through the proposed two-stage search.

Literature Review:
Imitate the best features in nature and produce a better quality of solution efficiently. Table 5 summarizes existing techniques of swarm intelligence based on the strategies together with their usages. Xu et al. BIB009 found particle swarm optimization (PSO) is faster than both artificial bee colony (ABC) and genetic algorithm (GA) because PSO can perform without any complicated evolution. Previous studies have also shown some drawbacks of ABC, which are the limited ability of exploitation, slow convergence speed, and low-quality solutions BIB007 . In the review of GA and PSO algorithms, Gandomi et al. BIB006 identified the main purposes of these techniques, which solved significant problems faster. Evolution Strategy (ES) Evolution Emphasize the use of normally distributed random mutations (main operator). Several operators needed to consider in the analysis.  Evolutionary Programming (EP) Evolution Use the self-adaptation principle to evolve the parameters on searching. No recombination operator and difficult to identify useful values for parameter tuning.  Gene Expression Programming (GEP) Evolution Extremely versatile and greatly surpasses the existing evolutionary techniques. Several genetic operators needed to function on selected chromosomes during reproduction.  BIB004 Genetic Algorithm (GA) Evolution Use genes with mechanisms to mimic survival of the fittest and inspire the genetics with the evolution of populations. Priority of the genetic strings. BIB008 Genetic Programming (GP) Evolution Can select variables and operators automatically then assemble into suitable structures. No clearly defined termination point in biological processes operating. BIB001 BIB003 Memetic Algorithm Evolution Useful on the property of global convexity in the search space. Genetic operators (crossover and mutation) needed to consider in the analysis. BIB002  BIB005