A STRATEGY FOR EVOLUTION OF ALGORITHMS TO INCREASE THE COMPUTATIONAL EFFECTIVENESS OF NP-HARD SCHEDULING PROBLEMS

We explored a method of applying techniques of inductive learning from artificial intelligence to partition a full problem space into smalle r exclusive problem spaces, and developed an evolving algorithm for ea ch problem space. In this approach we first create attributes to defin e a problem, and use them to cluster the problem space into classes. T o each class of problems, a 'suitable' evolved algorithm is developed to apply. By suitable here we mean that its level of complexity fits t he level of difficulty of a problem of a particular type. The purpose is to increase efficiency and effectiveness. In this work we selected a developed algorithm as the parent algorithm to generate an evolved a lgorithm. The methods used include the technique of maximum decreasing of impurity to construct a classification tree that provides systemat ic class descriptions. A problem of sequencing jobs of unequal importa nce in a set on a single processor in order to minimize total tardines s is provided to illustrate the problem-solving procedures.