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Adaptive memory

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

Frequency-based memory records how often moves or attributes occur so their historical frequency can influence search decisions.

Adaptive memory records search history so move frequency can influence later decisions. Frequency-based memory tracks how often specific moves occur and can penalize frequently used, unsuccessful, or less promising moves. section 6.2.1

Within tabu search, adaptive memory supports responsive exploration through two complementary records:

  • Recency-based memory records recently visited moves and helps prevent immediate revisits. section 6.2.1
  • Frequency-based memory records historical move counts across the search and guides longer-term diversification. section 6.2.1 section 6.7
  • Together, these records help local search move beyond local optimality while balancing exploration against exploitation. section 6.2 section 6.7

A frequency table for a small route search

In a four-city traveling-salesman search, a tabu structure can store recency values for temporarily forbidden moves alongside frequency counts showing how often each move has been used. The counts provide historical context when selecting later neighbors.

Implementation switch

A generic optalgotools TabuSearch solver exposes frequency-based memory through use_longterm=False by default. Enable the option while tuning max_iter, tabu_tenure, neighbor_size, and aspiration settings. section 6.4

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes
Defines historical frequency tracking, contrasts it with recency memory, and demonstrates counts within a tabu structure and solver configuration.
Examples
Uses a four-city traveling-salesman tabu structure and a Python Himmelblau-function solver with an optional long-term-memory setting.
What it assumes
Assumes local-search and tabu-search ideas while distinguishing short-term recency memory from long-term frequency memory.
What you can do after
Configure it via optalgotools by enabling long-term memory and tuning iteration, tenure, neighborhood, and aspiration parameters.
How it connects
Connects frequency-based memory to tabu search, local search, long-term memory, recency-based memory, aspiration criteria, and exploration.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.