Improving case-based reasoning in solving optimization problems using Bayesian optimization algorithm
The Case-Based Reasoning (CBR) solves problems by using the past problem solving experiences. How to apply these experiences depends on the type of the problem. The method presented in this paper tries to overcome this difficulty in CBR for optimization problems, using Bayesian Optimization Algorithm (BOA). BOA evolves a population of candidate solutions through constructing Bayesian networks and sampling them. After solving the problems through BOA, Bayesian networks describing solutions features are obtained. In our method, these Bayesian networks are stored in a case-base. For solving a new problem, the Bayesian networks of those problems which are similar to the new problem, are retrieved and combined. This compound Bayesian network is used for generating the initial population and constructing the probabilistic models of BOA in solving the new problem. Our method improves CBR in two ways: first, in our method, how to use the knowledge stored in the case-base is disregarding the problem itself and is universally; second, this method stores the probabilistic descriptions of the previous solutions in order to make the stored knowledge more flexible. Experimental results showed that in addition to the mentioned advantages, our method improved the solutions quality.
case-based reasoning bayesian optimization algorithm bayesian networks otimization