Modern algorithms are introduced to perform job scheduling in a professional way; it is Genetic algorithms
may be a world class optimization algorithm that look for improved performance by sampling areas of the solution space that have high probability of resulting in a good solution.
Self Adaption in Genetic Algorithms
, Towards a Practice of Autonomous Systems, MIT Press.
Solving the traveling salesman problem through genetic algorithms
with changing crossover operators.
To efficaciously solve this optimization problem, we propose a mixed search method based on the orthogonal genetic algorithm
and sensitivity analysis.
To do that, an approach based on genetic algorithms
has been employed.
An image registration approach using genetic algorithms
Metaheuristics motivated by idea of learning from past searches include ant colony optimization [6, 28, 10, 9, 19], evolutionary computation  and its special case, genetic algorithms
, to name just a few.
A decade later, genetic algorithms
became one of major rapidly developing fields of informatics and artificial intelligence.
1996) recommended a neural network model whereas Hegazy (1999) used genetic algorithms
by considering the minimum moment algorithm.
in Search, Optimization & Machine Learning, Addison Wesley.
The response surface method with genetic algorithm
(GA) is effectively used to determine the optimal initial estimates of eccentricity and ballistic coefficient using the TLEs of each time interval .