Research Article
[Retracted] Evolving Long Short-Term Memory Network-Based Text Classification
Algorithm 2
Evolving long short-term memory networks.
| (i) | Output: optimized population | | (ii) | Input: {initial parameters of MOGA} | | (iii) | begin | | (iv) | Obtain initial random solutions ; | | (v) | Call Algorithm 1 by considering ; | | (vi) | Sort according to ; | | (vii) | / Selection operator/ | | (viii) | ; | | | While do | | (ix) | / and indicate final generation and children elimination / | | (x) | Randomly select ;/ mutation point / | | (xi) | ; | | (xii) | for do | | (xiii) | Evaluate fitness of | | (xiv) | if , then | | (xv) | remove ; | | (xvi) | ; | | (xvii) | else | | (xviii) | ; | | (xix) | end | | (xx) | end | | / Mutation / | | | for crossover do | | | Consider two solutions randomly as and ;/ , , and are children / | | | ; | | | Computer fitness of | | | if then | | | remove ; | | | else | | | remove ; | | | end | | | end | | | end | | / Ranking / Apply nondominated sorting on ; | | | return | | | end |
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