A Selection Process for Genetic Algorithm Using Clustering Analysis

On Solving 0/1 Multidimensional Knapsack Problem with a Genetic Algorithm Using a Selection Operator Based on K-Means Clustering Principle

The growing need for profit maximization and cost minimization has made the optimization field very attractive to both researchers and practitioners. In fact, many authors were interested in this field and they have developed a large number of optimization algorithms to solve either academic or real-life problems. Among such algorithms, we cite a well-known metaheuristic called the genetic algorithm. This optimizer tool, as any algorithm, suffers from some drawbacks; like the problem of premature convergence. In this paper, we propose a new selection strategy hoping to avoid such a problem. The proposed selection operator is based on the principle of the k-means clustering method for the purpose of guiding the genetic algorithm to explore different regions of the search space. We have elaborated a genetic algorithm based on this new selection mechanism. We have then tested our algorithm on various data instances of the 0/1 multidimensional knapsack problem. The obtained results are encouraging when compared with those reached by other versions of genetic algorithms and those reached by an adapted version of the particle swarm optimization algorithm.

Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study, we compare the performance of the MSGA with a traditional GA and hybrid GA (HGA). The experimental results demonstrate that MSGA reaches the maximum executed branch statements in the lowest execution time and the smallest number of test cases compared to the GA and HGA.

Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Inspired by biological systems, swarm intelligence algorithms are widely used to solve multimodal optimization problems. In this study, we consider the hybridization problem of an algorithm based on the collective behavior of fish schools. The algorithm is computationally inexpensive compared to other population-based algorithms. Accuracy of fish school search increases with the increase of predefined iteration count, but this also affects computation time required to find a suboptimal solution. We propose two hybrid approaches, intending to improve the evolutionary-inspired algorithm accuracy by using classical optimization methods, such as gradient descent and Newton’s optimization method. The study shows the effectiveness of the proposed hybrid algorithms, and the strong advantage of the hybrid algorithm based on fish school search and gradient descent. We provide a solution for the linearly inseparable exclusive disjunction problem using the developed algorithm and a perceptron with one hidden layer. To demonstrate the effectiveness of the algorithms, we visualize high dimensional loss surfaces near global extreme points. In addition, we apply the distributed version of the most effective hybrid algorithm to the hyperparameter optimization problem of a neural network.

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.