Metaheuristic
A metaheuristic is a high-level strategy that guides or coordinates heuristic search for good solutions to optimization problems without guaranteeing an optimal result.
Collaborative Software Design: How to facilitate domain modeling decisions
A guiding heuristic, or meta-heuristic, is a higher-level heuristic that guides the selection or application of other heuristics.
- How it is used
- Guides facilitators and participants in selecting collaboration moves for modeling, engagement, sensemaking, and decisions.
- How deep it goes
- Defines metaheuristics and supplies many situational prompts, with worked facilitation examples rather than formal theory.
- Examples
- Uses architecture modeling, EventStorming timelines, Example Mapping, stakeholder buy-in, and Pros-Cons-and-Fixes discussions.
- What you can do after
- Apply it by selecting and adapting facilitation moves to help groups model, discuss tradeoffs, and progress decisions.
Advanced Algorithms and Data Structures
A metaheuristic is a general-purpose optimization strategy that guides the search for good solutions without guaranteeing an optimum.
- How it is used
- Guides optimization searches when local improvement alone may miss better regions of the solution landscape.
- How deep it goes
- Defines the strategy through uphill moves, cooling, populations, and genetic operators without guaranteeing an optimum.
- Examples
- Uses Mars mission food planning, simulated-annealing paths, genetic-algorithm populations, and biologically inspired optimization families.
- What you can do after
- Evaluate/understand how exploration, local improvement, cooling, and population operators trade search breadth against solution quality.
Read how Advanced Algorithms and Data Structures explains metaheuristic →
Optimization Algorithms: AI techniques for design, planning, and control problems
A high-level optimization strategy that guides or coordinates heuristic search to explore promising solutions without guaranteeing optimality.
- How it is used
- Frames search and optimization as guided heuristic exploration for practical solution quality and time requirements.
- How deep it goes
- Defines guidance, exploration, exploitation, and applicability, but stops short of specifying one complete algorithm or parameter-tuning procedure.
- Examples
- Uses bin packing, heuristic ordering, repeated solution construction, and solution recombination as concrete search examples.
- What you can do after
- Evaluate/understand when guided heuristic search can trade guaranteed optimality for practical solution time and robustness.
Related concepts
- genetic algorithmincludes · A genetic algorithm is an evolutionary optimization method that improves a population of candidate solutions through fitness-based selection and variation, often including recombination and mutation.
- ant colony optimizationincludes · Ant colony optimization is a population-based probabilistic optimization technique in which artificial agents use simulated pheromone trails to guide the search for good solutions.
- heuristicuses · A heuristic is a practical rule or approximate method for guiding judgment, search, or problem solving without guaranteeing an exact or optimal result.
- incongruence insightincludes · A heuristic that identifies a potential underlying issue when a person's words do not align with their actions or nonverbal cues.
- population-based searchincludes · A search approach that maintains and evolves a collection of candidate solutions rather than a single solution.
- trajectory-based searchincludes · A search approach that generates a sequence of candidate states by repeatedly moving from one state to another.
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