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But instead of mindlessly trying every one of them until a solution is found, evolutionary computing narrows down the search by using "genetic algorithms" to crop and refine the results after each iteration.As their name implies, genetic algorithms employ trial-and-error to mimic the way natural selection works in the living world.
In this paper, we present an improved technique using genetic algorithms to decompose arbitrarily shaped binary structuring elements.
PZT actuators, bonded symmetrically on the plate, are optimally grouped into two equivalent actuator sets using genetic algorithms to enhance controllability.
Weights are assigned to the various components of the potential field function and the weights are optimized using genetic algorithms to get an optimal path that satisfies the path constraints via a cost function.
(2015), the algorithm is enhanced by using genetic algorithms to explore the system and to estimate the potential benefits of each of these sources of information in foraging on different distributions of food.
This paper presents an optimization of a four-degrees-of-freedom quarter car seat and suspension system using genetic algorithms to determine a set of parameters to achieve the best performance of the driver.
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Izadi and Kimiagari (2014) designed a distribution network under uncertain demand using genetic algorithm to minimize various costs.
The basic steps of using genetic algorithm to solve the well pattern optimization model are as follows: 1.
In this paper, we will discuss the parameters settings using genetic algorithm to solve continuous network design problems (CNDP).
The gearbox design process can be improved by using genetic algorithm to determine the optimal values of gear pair parameters.
By using genetic algorithm to optimize parameters and control training error threshold, a good balance on accuracy/diversity will be achieved.
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