approximating the search distribution to the selection distribution in edas
In an Estimation of Distribution Algorithm (EDA) with an infinite sized population the selection distribution equals the search distribution. For a finite sized population these distributions are different. In practical EDAs the goal of the search distribution learning algorithm is to approximate the selection distribution. The source data is the selected set, which is derived from the population by applying a selection operator. The new approach described here eliminates the explicit use of the selection operator and the selected set. We rewrite for a finite population the selection distribution equations of four selection operators. The new equation is called the empirical selection distribution. Then we show how to build the search distribution that gives the best approximation to the empirical selection distribution. Our approach gives place to practical EDAs which can be easily and directly implemented from well established theoretical results. This paper also shows how common EDAs with discrete and real variables are adapted to take advantage of the empirical selection distribution. A comparison and discussion of performance is presented.
selection methods estimation distribution algorithms