Acceptance probability
Acceptance probability is the probability that an algorithm accepts a proposed state transition, including a transition that worsens its objective.
Optimization Algorithms: AI techniques for design, planning, and control problems
The probability that a stochastic optimization algorithm accepts a proposed state transition, including a worsening move.
- How it is used
- Controls whether simulated annealing accepts proposed worsening moves while searching for optimal or near-optimal solutions.
- How deep it goes
- Defines the exponential rule, random threshold, temperature effects, probability extremes, and a worked numerical example, but not formal proofs.
- Examples
- Uses a one-variable objective example, random thresholds, temperature updates, and acceptance-probability visualizations.
- What you can do after
- Apply it by calculating a candidate’s probability, drawing a threshold, and deciding whether simulated annealing accepts the transition.
Advanced Algorithms and Data Structures
Acceptance probability is the probability that an optimization algorithm accepts a proposed transition, particularly one that increases the cost.
- How it is used
- Guides simulated annealing’s decision to accept proposed transitions, including moves toward higher-cost states.
- How deep it goes
- Defines temperature- and cost-dependent acceptance, explains calibration and search effects, and contrasts early, middle, and late annealing behavior.
- Examples
- Uses an unconstrained search landscape and Figure 17.6 to show exploration, cooling, and eventual preference for better solutions.
- What you can do after
- Apply it via simulated annealing by calibrating the acceptance constant and interpreting temperature-driven exploration and convergence.
Read how Advanced Algorithms and Data Structures explains acceptance probability →
Related concepts
- locally optimal solutionaddresses · A locally optimal solution is a feasible point whose objective value is no worse than those of sufficiently nearby points, though a better solution may exist elsewhere.
- simulated annealingpart of · Simulated annealing is a stochastic optimization method that gradually reduces the probability of accepting worse solutions to escape local optima.
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