文件名称:
遗传算法（Genetic Algorithm Developed by Prof. Kalyanmoy Deb）
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文件大小: 28kb
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上传时间: 2014-10-03
详细说明： Genetic Algorithm Developed By : Prof. Kalyanmoy Deb with assistance from his Students. This is a GA implementation using binary and real coded variables. Mixed variables can be used. Constraints can also be handled. All constraints must be greater-than-equal-to type (g >= 0) and normalized (see the sample problem in prob1 in objective()). There are three sample input file (inp-r for real-coded variables only, inp-b for binary-coded variables only, and inp-rb for a mixed real and binary variables) which can be used to ru n this code. The template file for each input data file is also included (input-real, input-binary, and input-real+binary). Code your objective function and constraints at the end of the code (in objective()) Variable boundaries for real-coded variables can be fixed or flexible. Following selection opeartor is coded: Tournament selection: Set MINM=1 for minimization and -1 for maximization in objective(). For binary strings, single-point crossover and for real parameters simulated binary crossover (SBX) are used. Mutation: bit-wise for Binary coded GAs and polynomial mutation (with eta) for Real coded GAs Constraints are handled using Deb's paramater-less approach (see CMAME, 2000 paper) Niching allows restricted tournament selection. Recommended for complex and disconnected feasible regions. (Niching parameter of 0.1 is recommended.) The execution creates a file `result.out' which contains the input data and best solution obtained by the GA. The feasiblilty of the best solution and constraint values are also marked. The report.out contains population record of each generation. The file 'plot.out' contains a gnuplot-compatibale data file for plotting best, avg, and worst population fitness versus generation number. ...展开收缩
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