genetic algorithm


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genetic algorithm

n.
An algorithm that solves a problem using an evolutionary approach by generating mutations to the current solution method, selecting the better methods from this new generation, and then using these improved methods to repeat the process.
References in periodicals archive ?
Next, the optimization method using a genetic algorithm is developed and discussed.
In the optimization of the weight of the BP neural network, we combine the genetic algorithm, Tabu forbidden search algorithm and BP neural network in this paper.
The heart of genetic algorithm research began with Schaffer in the 1980s.
An evolution can be started by providing the target system's null architecture to the genetic algorithm.
Keywords: artificial immune system, intrusion detection, genetic algorithm, Minkowski distance
New comprehensive empirical forecast model uses genetic algorithm to better capture non-linear atmosphere events
Genetic algorithm can only solve unconstrained problem directly, while commonly problems except high constraint problem cannot be converted into unconstrained problems directly[10].
Conway and Venkataramanan (1994) examined the suitability of genetic algorithm for the dynamic layout problem (DLP).
The basic algorithm for parallel genetic algorithm can be described as below:
The representation of projects in genetic algorithm software may be loosely compared to biological chromosomes.
Before a genetic algorithm can be put to work on any problem, a method is needed to encode potential solutions to that problem in a form that a computer can process.
The genetic algorithm will be used to select subsets of shares, and sequence optimization will perform the index selection, evaluating the performance of each particular subset.

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