Research Techniques
Real world problems are often complex in relation to the number of interrelating factors which must be assessed in deciding upon a satisfactory solution. Finding the best possible solution means the user must consider every possible combination in order to ensure which one is best or optimal
Such problems are often termed combinatorial optimisation problems and are characterised by having an extremely large solution search space. The solution to the problem in hand satisfies some evaluation criteria decided upon before beginning the search process.
The search for an Optimal solution involves locating a global minimum in the search space of all potential solutions which satisfy the evaluation criteria. The following provides various techniques which have been used within my research so far.
Heuristic Search
Construction Heuristics
Improvement Heuristics
Hill Climbing
Meta-Heuristics
Hyper-Heuristics
Evolutionary Algorithms
Swarm based Algorithms
Neural Networks
Memetic Algorithms
Deterministic or Exact Methods
Branch and Bound Methods
Integer Programming
Linear Programming
Other Techniques
Achievement Curves
Reinforcement Learning
Fuzzy Logic
Potential researchers are encouraged to contact me for further details of these or indeed other leading edge research techniques can be applied to a real world industrial applications.