Genetic Algorithm: Reviews, Implementations, and Applications

Abstract

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology 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.

Artificial Intelligence in Classical and Quantum Photonics

“…Recently, Luo et al [110] combined a NN with a genetic algorithm (GA). [111,112] GAs are metaheuristic algorithms used to solve real-life complex problems belonging to different fields such as economics, engineering, politics, and management. They mimic the Darwinian theory of survival of the fittest in nature using as basic elements chromosome representation, fitness selection and biological-inspired operators such as selection, mutation, and crossover.…”

Intelligent Warehouse Picking Improvement Model for e-Logistics Warehouse Using Single Picker Routing Problem and Wave Picking

“…3 The steps of problem-solving using GA [38] Step -1. The initial chromosomal formation is used as input data from the genetic algorithm [38]. Chromosomes were obtained by random generator technique.…”

“…Step-2. After the chromosomes are formed, an evaluation will be carried out on the fitness values on each chromosome [38]. The evaluation stage will carry out the process of eliminating chromosomes that have a lower fitness value.…”

“…Step-3. The crossover process requires two chromosomes to be used as parents [38]. This process is useful for obtaining offspring that allow for better fitness values.…”

“…Step-4. Mutation process is done by swapping individuals on chromosomes [38]. The mutation process in this study was carried out by swap-based mutation, which means swapping two individuals in the chromosome randomly.…”