Abstract:
TIlls thesis presents a new hybrid algorithm for decision tree construction. The proposed
algorithm exploits the strengths of Evolutionary Algorithms and Simulated Annealing to
come up with a new multiobjective population-oriented simulated annealing technique for
decision tree construction that performs well on standard datasets of va repository.
We have investigated the issues related to decision tree construction, different
evolutionary techniques and simulated annealing technique. A brief survey of the existing
greedy algorithms and EA based hybrid systems for decision tree construction has also
been incorporated in this thesis.
The new hybrid algorithm, as presented in this thesis, explores the search space
structurally and sequentially, other than using genetic operators like mutation or crossover
to breed new decision trees. To avoid a complete search, the new algorithm utilizes
simulated annealing technique and restrains from searching regions of the search space
having a very low probability of containing an optimal solution. On the other hand, to
make the search process faster and parallel, separate sets of search agents (populations of
decision trees) are maintained for the current search points aild the suboptimal solutions
that have so far been discovered.
Finally the new system has been compared with the most recent genetic algorithm based
hybrid systems like GALE and GATree as well as the traditional greedy heuristic based
system, 01.5. The experimental results reveal the effectiveness and superiority of the new
system over the above-mentioned systems.
