Decision Maturity Framework: Introducing Maturity In Heuristic Search

Heuristics-based search methodologies normally work on searching a problem space of possible solutions toward finding a “satisfactory" solution based on “hints" estimated from the problem-specific knowledge. Research communities use different types of methodologies. Unfortunately, most of the times, these hints are immature and can lead toward hindering these methodologies by a premature convergence. This is due to a decrease of diversity in search space that leads to a total implosion and ultimately fitness stagnation of the population. In this paper, a novel Decision Maturity framework (DMF) is introduced as a solution to this problem. The framework simply improves the decision on the direction of the search by materializing hints enough before using them. Ideas from this framework are injected into the particle swarm optimization methodology. Results were obtained under both static and dynamic environment. The results show that decision maturity prevents premature converges to a high degree.

Authors:

• Ayed Salman
• Fawaz Al-Anzi
• Aseel Al-Minayes

Keywords:

• Heuristic Search
• hints
• Particle Swarm Optimization
• Decision Maturity Framework.

References:

[1] A. J Carlisle, (2000), Adapting PSO to Dynamic Environments,
International Conference on Artificial Intelligence (ICAI 2000), Las
Vegas ,.
[2] M. Clerc, (1999), The swarm and the queen: towards a deterministic and
adaptive particle swarm optimization, Proceedings of the Congress on
Evolutionary Computation (CEC99) (July) 1951-1957
[3] A. Dempster, (1968), Generalization of Bayesian inference. J. Roy.
Statist. Soc. B., 30, 205-247.
[4] Y. Shi, R. Eberhart, (1999), Empirical study of particle swarm
optimization, Proceedings of the Congress on Evolutionary
Computation (CEC99) (July) 1945-1950.
[5] Jurgen Branke, (1999), Evolutionary Approaches to Dynamic
Problems, A survey, Technical Report 387, Insitute AIFB, University
of Karlsruhe
[6] Philippe Smets, (1996), Imperfect Information: Imprecision and
Uncertainty. Uncertainty Management in Information Systems:
pp225-254
[7] Kohlas, J., & Monney, P. A. (1995). A mathematical theory of hints:
An approach to dempster-shafer theory of evidence. Lecture Notes in
Economics and Mathematical Systems No. 425. Springer- Verlag.
[8] Smets, P., & Kennes, R. (1994). The transferable belief model.
Artificial Intelligence, 66, 191-234.