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A modified GA with new features in chromosome encoding, mutation, crossover, selection operation as well as GA structure is proposed herein to optimize the multidimensional integration problem of precedence-constrained production planning and scheduling in multiple production line environment as described above.
In C. acetobutylicum, spo0A was deleted by using a replicative plasmid pETSPO capable of integrating into the chromosome through two rounds of crossover selection [36].
A population P = {Φ j : j ∈ 1,…, p} of p networks, a crossover (selection) rate q (1 − q) and mutation rate m with q, m ∈ [0; 1] are given.
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Making use of the NK fitness landscape model, we analyse the effects of mutation rate, crossover and selection pressure on the performance of DE.
Proposed algorithm utilizes the idea of fixed-length chromosome encoding scheme combined with expression vector and the modified initialization, mutation, crossover and selection operators to search for optimal reconfiguration structure.
In order to improve the overall performance of CMA-ES, the mutation, crossover and selection operators of DE have been incorporated into CMA-ES to synthesize the hybrid algorithm DCMA-EA.
This paper investigates a relation between the parallel efficiency of the coarse-grained PGA and genetic (crossover and selection) operators for the traveling salesman problem on an MIMD parallel computer.
The optimization process in DE is carried out with three basic operations: mutation, crossover and selection.
In the following text, the specific operations including initialization, mutation, crossover and selection are introduced.
The parameters are population size, probability of mutation, probability of crossover, and selection.
Reproduction is the process that copies the individuals that will participate in the crossover and selection processes, without modifying them.
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