Adaptive Mutation Operators for Evolutionary Algorithms

Evolutionary algorithms (EAs) are a class of stochastic search and optimization algorithms that are inspired by principles of natural and biological evolution. Although EAs have been found to be extremely useful in finding solutions to practically intractable problems, they suffer from issues like premature convergence, getting stuck to local optima, and poor stability. Recently, researchers have been considering adaptive EAs to address the aforementioned problems. The core of adaptive EAs is to automatically adjust genetic operators and relevant parameters in order to speed up the convergence process as well as maintaining the population diversity. In this thesis, we investigate adaptive EAs for optimization problems. We study adaptive mutation operators at both population level and gene level for genetic algorithms (GAs), which are a major sub-class of EAs, and investigate their performance based on a number of benchmark optimization problems. An enhancement to standard mutation in GAs, called directed mutation (DM), is investigated in this thesis. The idea is to obtain the statistical information about the fitness of individuals and their distribution within certain regions in the search space. This information is used to move the individuals within the search space using DM. Experimental results show that the DM scheme improves the performance of GAs on various benchmark problems. Furthermore, a multi-population with adaptive mutation approach is proposed to enhance the performance of GAs for multi-modal optimization problems. The main idea is to maintain multi-populations on different peaks to locate multiple optima for multi-modal optimization problems. For each sub-population, an adaptive mutation scheme is considered to avoid the premature convergence as well as accelerating the GA toward promising areas in the search space. Experimental results show that the proposed multi-population with adaptive mutation approach is effective in helping GAs to locate multiple optima for multi-modal optimization problems.