The challenge of identifying the global optimum in a space containing numerous local optima is a fundamental problem encountered by all systems capable of adapting and learning. GA offers a thorough search process for optimization.
Genetic Algorithms (GA) can be used to solve optimization
issues, whether they are continuous or discrete in nature. This study uses
binary coded GA approaches to function optimization, utilizing a variable
length representation scheme and updated genetic operators.
Genes and chromosomes constitute the fundamental components
of the binary genetic algorithm. The traditional binary genetic algorithm
encodes optimization parameters into binary code strings. A gene in GA is
represented as a binary digit. To create a population of individuals that
represent search space points using the evolution model, the search process
iteratively transforms a population of mathematical objects (typically
fixed-length binary character strings) with fitness values into a new
population of offspring objects. This is accomplished through the application
of Darwinian natural selection and operations that are patterned after natural
genetic operations such as crossover and mutation. Each individual is evaluated
using an objective function for fitness. Consequently, generational evolution
is inclined to yield fitter individuals and enhanced solutions within the
search space. This process continues until the problem is resolved or no
further improvements are observed.
Author (s) Details
Halah Ahmad Abd
Almeneem Mhamd
Department of Mathematics, Darb University College, Jazan University,
Jazan, 45142, Saudi Arabia.
Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v6/2573
