Memory models for improving tabu search with real continuous variables
This paper proposes that current memory models in use for tabu search algorithms are at best evolving, as opposed to adaptive, and that improvements can be made by considering the nature of human memory. By introducing new memory structures, the search method can learn about the solution space in which it is operating. The memory model is based on the transfer of events from episodic memory into generalised rules stored in semantic memory. By adopting this model, the algorithm can intelligently explore the solution space in response to what has been learned to date and continuously update the stored knowledge.