Linear and Combinatorial Optimizations by Estimation of Distribution Algorithms

Estimation of Distribution Algorithms (EDAs) is a new area of Evolutionary Computation. In EDAs there is neither crossover nor mutation operators. New population is generated by sampling the probability distribution, which is estimated from a database containing selected individuals of the previous generation. Different approaches have been proposed for the estimation of probability distribution. In this paper we provide a review of different EDA approaches and show how to apply UMDA with Laplace correction to Subset Sum, OneMax function and n-Queen problems of linear and combinatorial optimizations. The experimental results of the three problems comparing the performance of UMDA with that of Genetic Algorithm(GA) are provided. In our experiment UMDA outperforms GA for linear problems.