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2009•Computer Engineering and Applications JournalRequires access

New genetic algorithm to solve 0-1 knapsack problem

Ren Zhe

Open publisher page 3 citations

Abstract

It is well known that the GA is a guided random search and the guiding direction always aims at the family whose ancestors have schemata with high fitness.Based on the results,the crossover operation in GA is redesigned by using the principle of random uniform design sampling.Then a new GA called Genetic Algorithm based on Random Uniform Design Sampling is presented.The new GA is applied to solve the 0-1 knapsack question.Compared to simple GA and Good Point GA for solving this problem,the simulation results show that the new GA has superiority in speed,accuracy and overcoming premature.

About this research paper

What this paper is about

It is well known that the GA is a guided random search and the guiding direction always aims at the family whose ancestors have schemata with high fitness.Based on the results,the crossover operation in GA is redesigned by using the principle of random uniform design sampling.Then a new GA called Genetic Algorithm based on Random Uniform Design Sampling is presented.The new GA is applied to solve the 0-1 knapsack question.Compared to simple GA and Good Point GA for solving this problem,the simulation results show that the new GA has superiority in speed,accuracy and overcoming premature.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

It is well known that the GA is a guided random search and the guiding direction always aims at the family whose ancestors have schemata with high fitness.Based on the results,the crossover operation in GA is redesigned by using the principle of random uniform design sampling.Then a new GA called Genetic Algorithm based on Random Uniform Design Sampling is presented.The new GA is applied to solve the 0-1 knapsack question.Compared to simple GA and Good Point GA for solving this problem,the simulation results show that the new GA has superiority in speed,accuracy and overcoming premature.

Key concepts: Knapsack problem, Crossover, Genetic algorithm, Mathematical optimization, Point (geometry), Simple random sample, Computer science, Sampling (signal processing)

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