An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

Abstract

The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

A protein inspired RNA genetic algorithm for parameter estimation in hydrocracking of heavy oil

“…The detailed results of 2-dimensional functions f 1 − f 3 are listed in Table 3 and those of 10-dimensional functions f 4 − f 7 are listed in Table 4. Some results reported in literatures [17][18][19][20] are also listed in Tables 3 and 4 for comparing. From Table 3, the best solution of 30 runs on f 1 − f 3 is better than SSGA [17], PfGA [17] and IGA [17] greatly.…”

“…Some results reported in literatures [17][18][19][20] are also listed in Tables 3 and 4 for comparing. From Table 3, the best solution of 30 runs on f 1 − f 3 is better than SSGA [17], PfGA [17] and IGA [17] greatly. Especially to function f 2 , the proposed algorithm finds the true global optimum every time, which indicates the perfect capabilities of the PIRGA on global exploration and local exploitation.…”

“…Functions f 1 − f 3 are 2-dimentional and f 4 − f 7 are 10 dimensional. In order to compare results with those in literatures published, we do 30 independent runs, the same times as literatures [17,18], to each function. In the experiments, the parameters of the PIRGA are set as: population size N = 80, the probability of RNA-recoding operator P RNA-recoding = 0.8, the number of best group N best = 10, the number of maximal generation G max = 5000.…”

A novel multi-population cultural algorithm adopting knowledge migration

“…And then they replace old parent individuals to generate child individuals in mutation operation. Here, roulette selection method (Wang and Okazaki 2007) is adopted to randomly choose new parent individuals. Otherwise, if parent individuals are located in the cells with 'H', child individuals are generated by mutation operator within these cells.…”

Application of Improved Genetic Algorithms in Structural Optimization Design

“…Ahn et al [5] developed a memory-efficient elitist genetic algorithm for solving complex optimization problems quickly and effectively. Wang, RL and Okazaki, K. [6] presented an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Kaveh A and Kalatjari V. [7] performed size and topology optimization of trusses using a genetic algorithm, the force method, and some concepts of graph theory.…”