Evo search

Evo-Search

Runs a genetic-algorithm-style loop over candidate responses: generate a diverse initial population, score each against a rubric, then repeatedly select, crossover, and mutate to improve quality across generations. Output the top 3 final solutions.

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Step 0: Problem Intake & Rubric

Identify problem type, constraints, audience, and scope. If ambiguous, ask one question.

Rubric Construction

Detect which mode applies:

Mode	Trigger	Action
A: Auto	User gave only the problem	Generate 4–6 domain-appropriate criteria
B: Guided	User hinted at priorities	Generate rubric, weight toward stated priorities
C: Manual	User gave explicit criteria	Convert each into a scored rubric entry with anchors

In all modes: augment vague criteria into scorable definitions, and always add:

Overall Fitness (30%): "Would a knowledgeable expert prefer this over a competent but unremarkable response?" Scored holistically. Prevents narrow-criteria gaming.

Present the rubric to the user and wait for confirmation before proceeding.

Rubric format:

| Criterion      | Description              | Weight | Max |
|----------------|--------------------------|--------|-----|
| [Name]         | [Definition + anchors]   | X%     | 10  |
| Overall Fitness| Expert holistic score    | 30%    | 10  |
Weighted Total = Σ(score × weight)  [max = 10.00]

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Step 1: Initial Population

Generate 6 candidates (default) using these diversity frames, one per candidate:

#	Frame
1	Conventional / mainstream
2	Contrarian / challenges assumptions
3	First-principles / bottom-up
4	Analogy-led / draws from another domain
5	Risk-focused / emphasizes what could go wrong
6	Synthesis / combines multiple angles

Generate all candidates before scoring any. Then score each and display:

GENERATION 0
| # | Frame        | Fitness | Strength       | Weakness      |
|---|--------------|---------|----------------|---------------|
| 1 | Conventional | X.X     | [one phrase]   | [one phrase]  |

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Step 2: Evolution Loop

Default: 5 iterations. Repeat until stopping criteria are met.

2.1 Selection

Tournament selection: sample 2 candidates, keep the higher scorer. Repeat to get 4 parents. Elitism: carry the top 2 candidates forward untouched every generation.

2.2 Crossover

Produce 4 offspring (pair parents: 1+2, 3+4, 1+3, 2+4). For each pair, synthesize a child that inherits the key strength of each parent without their primary weaknesses. Rotate crossover strategies across iterations (trait synthesis → section splice → schema inheritance).

2.3 Mutation

Apply mutation to each offspring with probability 0.2 (~1 of 4 offspring per generation).

Mode	When to use	What to do
Exploitative (~60%)	Offspring has a diagnosable weak criterion	Target and fix that specific criterion
Exploratory (~40%)	No clear weakness, or population is converging	Random perturbation: swap rhetorical stance, domain lens, level of abstraction, or target audience

Multi-feature mutation: ~1 in 3 mutation events, mutate two features simultaneously. Use when the population has converged for 2+ generations, two criteria score equally low, or an exploratory mutation is being applied.

2.4 Evaluate & Merge

Score all offspring. Merge with current population. Keep top 6 by fitness score. Elites from 2.1 are guaranteed to survive.

2.5 Convergence Check

If top 3 candidates are within 0.4 fitness of each other AND content is substantially similar, inject 1–2 fresh randomly-framed candidates ("immigrants") before merging.

Display per-generation summary:

GENERATION [N]
| # | Origin        | Fitness | Δ   | Key Change         |
|---|---------------|---------|-----|--------------------|
| 1 | Elite         | X.X     | -   | -                  |
| 2 | Crossover A+B | X.X     | +Y  | [what changed]     |
Best: Candidate [N] (X.X)

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Stopping Criteria

Stop early if any condition is met:

• Max iterations reached (default: 5)
• Best fitness ≥ 9.0
• Best fitness improved less than 0.2 over 2 consecutive generations

Note the reason when stopping early.

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Step 3: Output

3.1 Contrast Table (show before full answers)

TOP 3 SOLUTIONS
| Rank | Score | Defining Strength    | Best Used When              |
|------|-------|----------------------|-----------------------------|
| #1   | X.X   | [one phrase]         | [context where #1 wins]     |
| #2   | X.X   | [one phrase]         | [context where #2 wins]     |
| #3   | X.X   | [one phrase]         | [context where #3 wins]     |

3.2 Full Solutions

Output the top top_k solutions in full (default: 3), labeled with rank and score.

3.3 Evolution Trace

EVOLUTION SUMMARY
| Generation | Best Fitness | Key Improvement     |
|------------|--------------|---------------------|
| 0 (init)   | X.X          | [note]              |
| ...        | ...          | ...                 |
Final: X.X / 10.0

3.4 Rubric Breakdown

Show per-criterion scores for the #1 solution only.

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Parameters

Parameter	Default	Range	Effect
population	6	4–10	Candidates per generation
iterations	5	1–10	Evolution cycles
mutation_rate	0.2	0–1	Fraction of offspring mutated
mutation_mode	mixed	exploitative / exploratory / mixed	Mutation strategy bias
multi_mutation	occasional	never / occasional / frequent	How often 2 features mutate at once
elites	2	1–3	Candidates preserved each generation
top_k	3	1–5	Final solutions shown in full
rubric_mode	auto	auto / guided / manual	How the rubric is built
show_trace	true	true / false	Show evolution summary

Users set parameters in plain language. Examples:

• "Quick run, 3 iterations" → iterations=3
• "Large population, thorough search" → population=8–10
• "Be more exploratory" → mutation_mode=exploratory
• "Just show me the winner" → top_k=1

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Scoring Guidance

Score honestly. A mediocre-but-coherent answer scores 5-6. Inflated scores destroy the selection signal and the algorithm stops working. The crossover step is where the most value is created. Synthesize genuine strengths; don't concatenate text.