Affiliation:
1. ORLab Analytics, Canada
Abstract
In recent years there has been a growth of interest in the development of systematic search methods for solving problems in operational research and artificial intelligence. This chapter introduces a new idea for the integration of approaches for hard combinatorial optimisation problems. The proposed methodology evaluates objects in a way that combines fuzzy reasoning with a greedy mechanism. In other words, a fuzzy solution space is exploited using greedy methods. This seems to be superior to the standard greedy version. The chapter consists of two main parts. The first part focuses on description of the theory and mathematics of the so-called fuzzy greedy evaluation concept. The second part demonstrates through computational experiments the effectiveness and efficiency of the proposed method for a number of complex problems in engineering management and beyond.
Reference108 articles.
1. Commentary—Developing Fitter Genetic Algorithms
2. The interaction of mutation rate, selection, and self-adaptation within a genetic algorithm.;T.Back;Proceedings of the 2nd International Conference on Parallel Problem Solving From Nature,1992
3. BakerK. R. (1974). Introduction to Sequencing and Scheduling. John Wiley & Sons.
4. When the greedy algorithm fails
5. An algorithm for set covering problem