Autor/Autorin: Adewumi Idowu Olugbenga
Adewumi, I.O was bagged B.Sc. degree in Industrial and Production Engineering from Faculty of Technology, University of Ibadan, Nigeria in 2009. He has M.Sc. in Agricultural and Environmental Engineering (Farm Power & Machinery Option) University of Ibadan, Nigeria in 2014. Currently he is a PhD Student of U.I.He lecture at Fed.Coll.of Agric.Ibadan
Time and resource management has become an issue in our day to day work. Routine problem of agricultural extension agents for dissemination of new technology to local farmers continue to be cumbersome as the time, money and other resources are scarce and limited. It is then important for effective agricultural extension agent to be able to maximize all this resources by using a shortest route in order to save cost and time with proper technology delivery.Genetic algorithm simulates the logic of Darwinian selection as observed in the biological evolutionary process (Cells´ division, DNA, Mutation, etc) to solve problems. They are based on one hand on a heuristic gradient ascension method (selection and crossover) and in another hand on a semi-random exploration method (Mutation). In this research work, application of genetic algorithms was explored for the optimization problem embodied in the transit problem of agricultural extension agents in disseminating new innovation and technological advancement in agriculture in order to achieve vision 2020 . An order representation for the cost matrix for 10 cities and chromosomes was used. The result revealed that GA can solve the problem
Time and resource management has become an issue in our day to day work. Routine problem of agricultural extension agents for dissemination of new technology to local farmers continue to be cumbersome as the time, money and other resources are scarce and limited. It is then important for effective agricultural extension agent to be able to maximize all this resources by using a shortest route in order to save cost and time with proper technology delivery.Genetic algorithm simulates the logic of Darwinian selection as observed in the biological evolutionary process (Cells' division, DNA, Mutation, etc) to solve problems. They are based on one hand on a heuristic gradient ascension method (selection and crossover) and in another hand on a semi-random exploration method (Mutation). In this research work, application of genetic algorithms was explored for the optimization problem embodied in the transit problem of agricultural extension agents in disseminating new innovation and technological advancement in agriculture in order to achieve vision 2020 . An order representation for the cost matrix for 10 cities and chromosomes was used. The result revealed that GA can solve the problem
Über den Autor
Adewumi, I.O has his B.Sc. degree in Industrial and Production Engineering from Faculty of Technology, University of Ibadan, Nigeria in 2009. He completed his M.Sc. degree in Agricultural and Environmental Engineering (Farm Power & Machinery Option) University of Ibadan, Nigeria in 2014.Currently he is a lecturer at Fed. College of Agric Ibadan
Klappentext
Time and resource management has become an issue in our day to day work. Routine problem of agricultural extension agents for dissemination of new technology to local farmers continue to be cumbersome as the time, money and other resources are scarce and limited. It is then important for effective agricultural extension agent to be able to maximize all this resources by using a shortest route in order to save cost and time with proper technology delivery.Genetic algorithm simulates the logic of Darwinian selection as observed in the biological evolutionary process (Cells' division, DNA, Mutation, etc) to solve problems. They are based on one hand on a heuristic gradient ascension method (selection and crossover) and in another hand on a semi-random exploration method (Mutation). In this research work, application of genetic algorithms was explored for the optimization problem embodied in the transit problem of agricultural extension agents in disseminating new innovation and technological advancement in agriculture in order to achieve vision 2020 . An order representation for the cost matrix for 10 cities and chromosomes was used. The result revealed that GA can solve the problem
