Academic literature on the topic 'Reversed roulette wheel selection algorithms'

Scheduling lectures is not something easy, considering many factors that must be considered. The factors that must be considered are the courses that will be held, the space available, the lecturers, the suitability of the credits with the duration of courses, the availability of lecturers' time, and so on. One algorithm in the field of computer science that can be used in lecture scheduling automation is Genetic Algorithms. Genetic Algorithms can provide the best solution from several solutions in handling scheduling problems and the selksi method used is roulette wheel. This study produces a scheduling system that can work automatically or independently which can produce optimal lecture schedules by applying Genetic Algorithms. Based on the results of testing, the resulting system can schedule lectures correctly and consider the time of lecturers. In this study, the roulette wheel selection method was more effective in producing the best individuals than the rank selection method.

Orienteering problem (OP) is an NP-Hard graph problem. The nodes of the graph are associated with scores or rewards and the edges with time delays. The goal is to obtain a Hamiltonian path connecting the two necessary check points, i.e. the source and the target along with a set of control points such that the total collected score is maximized within a specified time limit. OP finds application in several fields like logistics, transportation networks, tourism industry, etc. Most of the existing algorithms for OP can only be applied on complete graphs that satisfy the triangle inequality. Real-life scenario does not guarantee that there exists a direct link between all control point pairs or the triangle inequality is satisfied. To provide a more practical solution, we propose a stochastic greedy algorithm (RWS_OP) that uses the roulette wheel selectionmethod, does not require that the triangle inequality condition is satisfied and is capable of handling both complete as well as incomplete graphs. Based on several experiments on standard benchmark data we show that RWS_OP is faster, more efficient in terms of time budget utilization and achieves a better performance in terms of the total collected score ascompared to a recently reported algorithm for incomplete graphs.

This research investigates using genetic algorithms (GA) to optimize production scheduling in Medan's shoe industry. The study compares traditional manual and First Come First Serve (FCFS) methods against a GA approach, incorporating selection variations such as Boltzmann, Fitness Uniform Selection Scheme (FUSS), Exponential Rank Selection, and Roulette Wheel Selection. The optimal production order is derived from the chromosome with the highest fitness. Results indicate that GA with FUSS selection significantly reduces production time from 73,630 minutes to 45,650 minutes, achieving a 35% improvement in efficiency. This optimization is attributed to FUSS’s ability to maintain a diverse population, preventing premature convergence and ensuring a broader solution for space exploration. Additionally, it was found that using a smaller population size relative to the number of generations yields better optimization results. The study also demonstrates that while Roulette Wheel Selection shows more variability, it achieves higher optimization over time than FCFS. The practical implications of these findings are substantial for the shoe industry, including faster production cycles, better resource allocation, and an enhanced ability to meet customer demands. These benefits are exemplified by implementing the SISPROMA application, an innovative production scheduling information system that leverages machine learning to optimize scheduling in the manufacturing industry. This study provides valuable insights into applying genetic algorithms for production scheduling, highlighting their potential to enhance operational efficiency and reduce costs. Future research should explore additional optimization techniques and real-world applications to validate and extend these findings, ensuring broader applicability and continuous improvements in manufacturing efficiency.

One of the major difficulties in software testing is the automatic generation of test data that satisfy a given adequacy criterion. This paper presents an automatic test data generation technique that uses a genetic algorithm (GA), which is guided by the data flow dependencies in the program, to search for test data to cover its def-use associations. The GA conducts its search by constructing new test data from previously generated test data that are evaluated as effective test data. The approach can be used in test data generation for programs with/without loops and procedures. The proposed GA accepts as input an instrumented version of the program to be tested, the list of def-use associations to be covered, the number of input variables, and the domain and precision of each input variable. The algorithm produces a set of test cases, the set of def-use associations covered by each test case, and a list of uncovered def-use associations, if any. In the parent selection process, the GA uses one of two methods: the roulette wheel method or a proposed method, called the random selection method, according to the user choice. Finally, the paper presents the results of the experiments that have been carried out to evaluate the effectiveness of the proposed GA compared to the random testing technique, and to compare the proposed random selection method to the roulette wheel method.