Genetic algorithms (GAs) are classical evolutionary computation methods, which have a wild application prospect. This paper proposes an improved genetic algorithm, named the isoline genetic algorithm (IGA), for numerical optimization. The proposed algorithm utilizes the population to model isolines of fitness in the search space. These isolines can be used to depict the fitness landscape in the current search area and direct the search process. IGA predicts the location of the peak by calculating the centroids of isolines, which will be probabilistically accepted into the population. Numerical experiments on thirteen benchmark functions reveal the effectiveness and efficiency of IGA. The experimental results indicate improvements in both convergence speed and solution accuracy.
['Ying Lin', 'Jun Zhang']
An Isoline Genetic Algorithm