genetic algorithm


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genetic algorithm

a computer-simulated evolutionary sequence used in cluster analysis.

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.
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The simulation models and optimization genetic algorithm are both developed and programmed in Matlab Environment so that the program can read the hourly cooling loads, outdoor air conditions, and utility cost structure from an excel sheet as an user's input file and then write the results back into the same or other excel sheet as user's output file.
In this paper, we will redefine the fitness of genetic algorithm and population fitness.
We chose the NSGA-II genetic algorithm developed by Deb (Deb, Pratap, Agarwal, & Meryarivan, 2002) for our basic genetic algorithm solver because of this algorithm's computational efficiency.
Gonzalez and Dasgupta in [2] used sequential niching technique with the genetic algorithm to generate the rules.
Automotive design companies use genetic algorithms to determine materials and shapes for faster, lighter, fuel-efficient vehicles.
Penalty function is one of the key factors of genetic algorithm to solve constraint problems, which can be denoted by [12]
In this paper, we present a genetic algorithm approach to obtain the best layout from the population of the initial layouts of the multi-factor user interface components layout problem for the example task under consideration as given in Peer et al.
In this paper, they have proposed a novel approach to cell image segmentation under severe noise conditions by combining kernel-based dynamic clustering and a parallel genetic algorithm.
The portfolio recommendation produced by genetic algorithm software will have a graphical element--usually visualized by bubble combinations on charts.
Toolpath Optimizer was designed on the base of theoretical and empirical knowledge acquired from genetic algorithm problematic and is designed for optimization of tool paths.
Out of these rules, the genetic algorithm is used to find the optimal investment rules.

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