To browse Academia. This paper presents an approach utilizing Genetic Algorithms GAs for optimizing routing in parallel computer interconnection networks. The focus is on dynamically balancing routing paths to enhance performance in heterogeneous processing environments. Results indicate significant improvements in computational efficiency and load distribution across networked systems, making a compelling case for the adoption of GAs in network routing applications.
By performing various tests on documents Shredded-1, Shredded-2 and Shredded-3, I will compare the average and best fitness of various chromosomes using the Uniform Ordered Crossover and One-Point Crossover. Moreover, scores of each generation are calculated to ensure the algorithm satisfies Charles Darwin theory of evolution.
The various tests in the Genetic Algorithms are conducted over various population sizes. Crossover and Mutations are performed on these randomized populations and offspring are generated. Earlier researches focused on finding optimal crossover or mutation rates, which vary for different problems, and even for different stages of the genetic process in a problem. This paper investigates the optimal cross-over probabilities and mutation probabilities for the optimum performance of GA.
Cross over probability are positively associated with the mutation probability in the implementation of GA but correlation is not significant. However, self-adapting control parameters also give better results. Further, the Inverted Displacement mutation operator introduced by Kusum and Hadush has a great potential for future research along with the crossover operators.
Genetic Algorithms GAs are robust search and optimization techniques that were developed based on ideas and techniques from genetic and evolutionary theory. Today GAs is used for optimization of diverse problems in various domains. For today's more complex problems, to better represent reality, heuristics like GAs have increased in importance.
