Study on Application of Genetic Algorithm in Discrete Variables Optimization
Zhizhong Mao
Abstract
Zhizhong Mao
Abstract
According to lots of discrete variable optimization problems in practice,the defects of applying continuous variable optimization to solve discrete variable optimization problems were studied.The characteristics of discrete variable optimization and genetic algorithm were associated.Thus,the discrete crossover operator and discrete mutation operator were proposed to make the genetic operator search in discrete space.Based on the theory of linear search,the discrete leading operator was proposed so as to improve the local searching capability of genetic algorithm,that led the population to local optimization and implemented rapid discrete searching.The study on two practical discrete variable optimization problems proves the validity of this algorithm in solving discrete variable optimization problems.
OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
According to lots of discrete variable optimization problems in practice,the defects of applying continuous variable optimization to solve discrete variable optimization problems were studied.The characteristics of discrete variable optimization and genetic algorithm were associated.Thus,the discrete crossover operator and discrete mutation operator were proposed to make the genetic operator search in discrete space.Based on the theory of linear search,the discrete leading operator was proposed so as to improve the local searching capability of genetic algorithm,that led the population to local optimization and implemented rapid discrete searching.The study on two practical discrete variable optimization problems proves the validity of this algorithm in solving discrete variable optimization problems.
Key concepts: Discrete optimization, Crossover, Discrete space, Discrete variable, Continuous optimization, Mathematical optimization, Operator (biology), Variable (mathematics)