Towards solving practical problems of large solution space using a novel pattern searching hybrid evolutionary algorithm – An elastic optical network optimization case study

The Practical Use of Problem Encoding Allowing Cheap Fitness Computation of Mutated Individuals

“…One of the assumptions of using FFE as a fair computation load measure is that the computation load required to compute a single fitness value is the same or, at least, similar. The research presented in [14] show that this assumption is not always true. For instance, the computation load may be dependent on the genotype.…”

“…The issue of fair computation load measurement is also addressed in [14]. One of the assumptions of using FFE as a fair computation load measure is that the computation load required to compute a single fitness value is the same or, at least, similar.…”

“…where X, X mut -individuals genotypes cost(X) -the computation load that must be paid to compute the fitness of X without any prior knowledge f itInf o(X) -the additional information about data produced during X fitness value calculation process (including X fitness value if necessary) cost(X, addInf o) -the computation load necessary to compute the fitness of X with additional information taken from the other fitness value calculation operation dif f r(X, Y ) -the number of genotype positions for which genotype X is different than the genotype Y size(X) -the number of genotype positions in genotype X If an additional information (from another fitness value computation operation) is available, then the computation load necessary for the fitness value computation will be significantly lower than if the same computation was done without the additional knowledge (inequality ( 13) is true). Condition (14) guarantees that the additional information is supported by the fitness calculation process for similar genotype.…”

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

“…[79]: solves the RSA problem in flex-grid networking producing useful insights into network design. [80]: solves the RSA problem with joint anycast and unicast demands. Ant colony optimization [9]: RWA solution with great robustness and adaptability to varying network and traffic conditions.…”

“…Furthermore, the RSA problem has also been solved in [78] with GAs for multicast flows considering different selection and crossover strategies, and in [86] with tabu search techniques with the aim of providing dedicated path protection. Another coevolutive approach is introduced in [80] to solve the RSA problem with joint anycast and unicast demands, outperforming previous proposals based on tabu search [87] and simulated annealing [124] approaches.…”