A hybrid algorithm based on genetic algorithm and plant growth simulation algorithm
Xu Liang, Tao Mu, Huang Ming
Abstract
Xu Liang, Tao Mu, Huang Ming
Abstract
In order to solve the deceptive and pre-maturity problem of genetic algorithm, we combined plant growth simulation algorithm with genetic algorithm, so a new hybrid genetic algorithm occurred. Because of the searching way is better both in direction and randomness balance, which decided by plant hormone in plant growth simulation algorithm, so the deceptive problem of genetic algorithm is well settled and we got the globally optimal solution in a relatively efficient way. The simulation results show that very nice effects are obtained.
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In order to solve the deceptive and pre-maturity problem of genetic algorithm, we combined plant growth simulation algorithm with genetic algorithm, so a new hybrid genetic algorithm occurred. Because of the searching way is better both in direction and randomness balance, which decided by plant hormone in plant growth simulation algorithm, so the deceptive problem of genetic algorithm is well settled and we got the globally optimal solution in a relatively efficient way. The simulation results show that very nice effects are obtained.
Key concepts: Algorithm, Genetic algorithm, Population-based incremental learning, Randomness, Computer science, Cultural algorithm, Hybrid algorithm (constraint satisfaction), Algorithm design