Description: Genetic Algorithms in a function application, which includes coding, crossover and mutation, select details such as programming, genetic algorithm is useful for learning.
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nGA for fun\Aimngafun.asv
...........\Aimngafun.m
...........\Aimniche.asv
...........\Endngafun.asv
...........\Endngafun.m
...........\Endniche.asv
...........\Evolngafun.m
...........\Evolniche.asv
...........\Initngafun.asv
...........\Initngafun.m
...........\Initniche.asv
...........\Mainngafun.m
...........\Mainniche.asv
...........\bestparam1.mat
...........\bestparam2.mat
...........\firstAimngafun.asv
...........\firstAimngafun.m
...........\firstAimniche.asv
...........\ngafun.asv
...........\ngafun.m
...........\niche.asv
...........\source\AdapCross.m
...........\......\Code.asv
...........\......\Code.m
...........\......\Cross.asv
...........\......\Cross.m
...........\......\Decode.m
...........\......\Mutation.asv
...........\......\Mutation.m
...........\......\ReadMe.txt
...........\......\Select.asv
...........\......\SelectMax.m
...........\source
nGA for fun
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