TABLE OF CONTENTS Genetic algorithm (GA) is a population based search method that is widely used due to its ease of implementation, intuitiveness and its ability to solve highly non-linear optimization problems. GA is a stochastic search procedure for combinatorial optimization problems based on the mechanism of natural selection (Malve & Uzsoy, 2007). Genetic algorithm is a particular class of evolutionary algorithms that uses techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (Youssef, Sait, & Adiche, 2001). The fitness function measures the quality of the represented solution, in this chapter, the solution to the missing data estimation error equation as described above. In this chapter, GA represents the missing variables with binary strings of 0's and 1's and these are referred to as chromosomes.
Kao, Zahara, and Kao (2008) hybridized genetic algorithm and particle swarm optimization (PSO) for global optimization of multi-modal functions. This hybrid system incorporates ideas from GA and PSO and generates individuals in a new generation by crossover and mutation operations which originate from GA as well as notions from PSO such as recalling the best in the group and the best gene that the individuals themselves have encountered. The experimental investigations utilizing a group of 17 multi-modal test functions shows that the hybrid GA-PSO method is superior to the other four search methods studied in terms of solution quality and convergence rates. The main shortcoming with this approach is that it does not make physical sense because there is...
