Research Article
The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators
Algorithm 1
The general framework of EA2DGO.
(1) Begin | (2) Input: NP, NOP, , CR, OMR, Max_Fes, hl, tl, times, , and ; where NP denotes the size | of function optimization population; NOP denotes the size of operator generating | population; denotes scaling factor; CR denotes the probability of crossover; OMR denotes | the probability of Mutation for operators in operator generating population; Max_FEs | denotes the max number of function calls; hl is the of head length of chromosome in new | encoding scheme MEP; tl is the tail length; times denotes the number of repeat times which | randomly select individuals for mutation manipulation from population in unit of function | optimization; is the terminal symbol set; is the function symbol set. | (3) | (4) Create the function optimization population , , | (5) Create the genetic operators population , , | (6) , , | (7) Evaluate , , | (8) For to Max_FES Do | (9) Find the best in the function optimization population | (10) Call the unit of function optimization | (11) If Then | (12) Call the unit of automatically designing genetic operators | (13) End If | (14) | (15) End For | (16) Output | (17) End |
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