Genetic algorithms—a class of stochastic population-based optimization techniques—have been widely realized as the effective tools to solve complicated optimization problems ...
A custom genetic algorithm was developed and implemented to solve multiple objective multi-state reliability optimization design problems. Many real-world engineering design proble...
We present a hybrid Genetic Algorithm that incorporates the Generalized Partition Crossover (GPX) operator to produce an algorithm that is competitive with the state of the art for...
Genetic algorithms (GAs) are stochastic search methods that have been successfully applied in many search, optimization, and machine learning problems. Their parallel counterpart (...
Parallel genetic algorithms (PGAs) have been developed to reduce the large execution times that are associated with serial genetic algorithms (SGAs). They have also been used to s...
Lee Wang, Anthony A. Maciejewski, Howard Jay Siege...