| name | pbs - Population Based Search Orderings |
| description | Maintain multiple candidate solutions simultaneously — evolutionary approaches, beam search, iterative deepening. |
| output | {"format":"prose"} |
Population Based Search Orderings
Input: $ARGUMENTS
Overview
Instead of pursuing one solution, maintain a population of candidates. Evaluate, select, recombine, and iterate. Finds better solutions than single-track search when the solution space is large and deceptive.
Ordering Rules
Rule 1: Parallel Evaluation
- Evaluate all candidates in the population before selecting
- Don't commit to one path prematurely
- When: multiple plausible approaches, uncertain which is best
Rule 2: Select and Recombine
- Keep the best candidates, combine their strengths
- "Take the best parts of each approach" — not metaphorical, literal
- When: solutions have modular components that can be mixed
Rule 3: Iterative Deepening
- Search to depth 1, then depth 2, then depth 3...
- Guarantees finding the shallowest solution while managing memory
- When: unknown solution depth, memory constraints
Rule 4: Beam Search — Top-K
- At each step, keep only the K best candidates
- Expand each, evaluate, keep top K again
- When: space too large for full exploration, need approximate best
Rule 5: Mutation — Random Perturbation
- After selecting best candidates, randomly modify some
- Prevents premature convergence on local optima
- When: stuck, all candidates too similar
Application
Step 1: Initialize Population
- Generate diverse starting candidates (don't start all the same)
Step 2: Evaluate All
- Score each candidate on the objective
Step 3: Select, Recombine, Mutate
- Keep top performers, combine, add random variation
- Repeat until convergence or budget exhausted
When to Use
- Complex optimization, creative ideation, strategy selection
- When one approach feels risky
Verification
