Linkage Learning Based on Local Optima
Genetic Algorithms (GAs) are categorized as search heuristics and have been broadly applied to optimization problems. These algorithms have been used for solving problems in many applications, but it has been shown that simple GA is not able to effectively solve complex real world problems. For proper solving of such problems, knowing the relationships between decision variables which is referred to as linkage learning is necessary. In this paper a linkage learning approach is proposed that utilizes the special features of the decomposable problems to solve them. The proposed approach is called Local Optimums based Linkage Learner (LOLL). The LOLL algorithm is capable of identifying the groups of variables which are related to each other (known as linkage groups), no matter if these groups are overlapped or different in size. The proposed algorithm, unlike other linkage learning techniques, is not done along with optimization algorithm; but it is done in a whole separated phase from optimization search. After finding linkage group information by LOLL, an optimization search can use this information to solve the problem. LOLL is tested on some benchmarked decomposable functions. The results show that the algorithm is an efficient alternative to other linkage learning techniques.
KeywordsLinkage Learning Optimization Problems Decomposable Functions
Unable to display preview. Download preview PDF.
- 2.Newman, J.: Electrochemical Systems, 2nd edn. Prentice-Hall, Englewood Cliffs (1991)Google Scholar
- 3.Hillman, A.R.: Electrochemical Science and Technology of Polymers, vol. 1, ch. 5. Elsevier, Amsterdam (1987)Google Scholar
- 4.Miller B.: Geelong, Vic., February 19-24; J. Electroanal. Chem., 168 (1984)Google Scholar
- 5.Jones: personal communication (1992)Google Scholar
- 6.Pelikan, M., Goldberg, D.E.: Escaping hierarchical traps with competent genetic algorithms. In: Genetic and Evolutionary Computation Conference, GECCO, pp. 511–518 (2001)Google Scholar
- 7.Pelikan, M., Goldberg, D.E.: A hierarchy machine: Learning to optimize from nature and humans. Complexity 8(5) (2003)Google Scholar
- 10.Stuart, R., Peter, N.: Artificial Intelligence: A Modern Approach, 2nd edn., pp. 111–114. Prentice-Hall, Englewood Cliffs (2003)Google Scholar