Convergence analysis of hidden genes genetic algorithms in space trajectory optimization
This study presents a convergence analysis that proves hidden genes genetic algorithms (HGGAs) generate a sequence of solutions with the limit value of the global optima. For an analytical proof, the homogeneous finite Markov models of different mechanisms are derived, and the convergence of the HGGAs with tag evolution mechanisms are investigated. The optimization problem is considered a maximization problem with strictly positive values for the objective function. The stochastic dependency between successive populations is created by applying selection, mutation, and crossover operators to the current population to produce the next population. The GA is a stochastic process in which the state of each population only depends on the state of the immediate predecessor population.
Journal of Aerospace Information Systems
Convergence analysis of hidden genes genetic algorithms in space trajectory optimization.
Journal of Aerospace Information Systems,
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