Using response surface design to determine the optimal parameters of genetic algorithm and a case study

Abstract

Article title: Using response surface design to determine the optimal parameters of genetic algorithm and a case study Usage guidelinesThis version is made available online in accordance with publisher policies. To see the final version of this paper, please visit the publisher's website (a subscription may be required to access the full text).Before reusing this item please check the rights under which it has been made available. Some items are restricted to non-commercial use. Please cite the published version where applicable. Using response surface design to determine the optimal parameters of genetic algorithm and a case study Ibrahim Abstract: Genetic algorithms are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP hard problems. This algorithm includes a number of parameters whose different levels affect the performance of the algorithm strictly. The general approach to determine the appropriate parameter combination of genetic algorithm depends on too many trials of different combinations and the best one of the combinations that produces good results is selected for the program that would be used for problem solving. A few researchers studied on parameter optimisation of genetic algorithm. In this paper, response surface depended parameter optimisation is proposed to determine the optimal parameters of genetic algorithm. Results are tested for benchmark problems that is most common in mixed-model assembly line balancing problems of type-I (MMALBP-I). Using response surface design to determine the optimal parameters of genetic algorithm and a case studyGenetic algorithms are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP hard problems. This algorithm includes a number of parameters whose different levels affect the performance of the algorithm strictly. The general approach to determine the appropriate parameter combination of genetic algorithm depends on too many trials of different combinations and the best one of the combinations that produces good results is selected for the program that would be used for problem solving. A few researchers studied on parameter optimisation of genetic algorithm. In this paper, response surface depended parameter optimisation is proposed to determine the optimal parameters of genetic algorithm. Results are tested for benchmark problems that is most common in mixed-model assembly line balancing problems of type-I (MMALBP-I).

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

“…Many types of assembly lines have been studied and various types of solution approaches suggested to solve these complex problems (Kucukkoc et al 2013a).…”

Direct reliability-based design optimization of uncertain structures with interval parameters

“…The outer layer GA terminates when the absolute difference between the objective mid-point value of the optimal solution and the average value of the current population is less than 10 −3 for 10 consecutive generations. GA involves a number of parameters, different levels of which greatly affect its performance (Kucukkoc et al, 2013). The parameter combination of the nested GA for solving the numeric example is determined based on many trials of different parameter combinations, and the one producing the best results is selected for the program (Table 1).…”

Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines

“…This process continues until all tasks are assigned. [5,4,7], [5,9,10], [8,7,12], [3,6,8], [9,10,13,16], [], [11,12], [11,14,15], []]…”

“…C A B C F E D [ [1,6,4], [3,2], [1,4,7], [2,5,9], [5,11], [9,7,8], [3,6,12], [10,13,16,8], [], [10,12], [11,15,14], []]…”