A non-revisiting Genetic Algorithm with adaptive mutation for Function Optimization

Abstract

Genetic Algorithm (GA) is a robust and popular stochastic optimization algorithm for large and complex search spaces. The major disadvantages of Genetic Algorithms are premature convergence and revisits to individual solutions from search space. In other words Genetic algorithm is a revisiting algorithm that leads to duplicate function evaluations which is a clear waste of time and computational resources. In this paper, a non-revisiting genetic algorithm with adaptive mutation is proposed for the domain of function optimization. In this algorithm whenever a revisit occurs, the underlined search point is replaced with a mutated version of the best/random (chosen probabilistically) individual from the GA population. Moreover, the suggested approach is not using any extra memory resources to avoid revisits. To test the power of the method, the proposed non-revisiting algorithm is evaluated using nine benchmarks functions. The performance of the proposed genetic algorithm is superior as compared to simple genetic algorithm as confirmed by the experimental results.

Keywords: Function optimization, Genetic Algorithm, Non-Revisiting, adaptive mutation.