Genetic Algorithm: Reviews, Implementations, and Applications

Abstract

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation

A hybrid approach to enhanced genetic algorithm for route optimization problems

“…Another approach is to find an operative genetic algorithm which finds the optimal path using the method of fuzzy logic [12]. Generally genetic operation starts with a selection operator which picks the most suitable chromosomes to produce the ideal solution [13], [14]. Then the operators such as crossover and mutation are applied to prevent early convergence of solutions [15]- [18].…”

Enhancing Customer Relationship Management Using Fuzzy Association Rules and the Evolutionary Genetic Algorithm

“…The concept threshold -common item groups that are generated by the evolutionary algorithm -is used in the additional pertinent work provided in [3] to develop the quantitative dataset-based rules. In this example, crossover and mutation are used to unify the rule in various ways and can detect the co-occurrence of item sets.…”

“…These algorithms are widely used in mining important data to determine association rules. They are also used to find association rules in practical issues such as business databases and fraud detection [3].…”

Genetic Algorithm untuk Menyelesaikan Russia-20-Nodes-TSP Instance

“…Parameter-parameter Genetic Algorithm yang digunakan pada penelitian ini adalah sebagai berikut: a) Crossover Probability Crossover Probability merupakan parameter yang menyatakan seberapa besar peluang sepasang orang tua akan melakukan perkawinan silang (Alam et al, 2020). Rentang nilai untuk parameter ini adalah dari 0 sampai dengan 1.…”