⚡ Optimization

Evolve optimization strategies to minimize benchmark functions.

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Prerequisites

1. VEOX Server: Start the local VEOX server (requires Docker):

   docker run -d \
     --name veox-enclave-server \
     -p 8090:8090 \
     714044927654.dkr.ecr.us-east-2.amazonaws.com/doug/single_enclave/veox-enclave-server:latest
   

   See the Quick Start for detailed server setup, health checks, and Docker Compose instructions.

2. Python SDK: Install the veox package via PyPI:

   pip install veox
   

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What It Does

The optimization family evolves multi-stage optimization pipelines that discover the global minimum of mathematical benchmark functions:

Initializer → Mutator → Crossover → Selection → Adaptation

Uses server-side benchmark functions (Rastrigin, Ackley, Rosenbrock, etc.).

Quick Start

from veox import VeoxEvolver

evolver = VeoxEvolver("optimization")
evolver.fit(max_generations=5, population_size=30)
print(f"Best Value: {evolver.best_fitness_:.6f}")
print(f"Pipeline:   {evolver.best_pipeline_}")

Full Example

VEOX FreeVEOX Pro

from veox import VeoxEvolver

# 1. Connect
evolver = VeoxEvolver("optimization", api_url="http://127.0.0.1:8090")
evolver.health_check()

# 2. Evolve
evolver.fit(
    max_generations=10,
    population_size=50,
    timeout_per_eval=60,
    max_poll_time=600,
)

# 3. Inspect
print(f"Best Value:  {evolver.best_fitness_:.6f}")
print(f"Pipeline:    {evolver.best_pipeline_}")
print(f"Evaluations: {evolver.result_.total_evals}")

# 4. Save
evolver.save("optimization_results.json")

from veox import VeoxEvolver

# 1. Connect
evolver = VeoxEvolver("optimization", api_url="http://127.0.0.1:8090")
evolver.health_check()

# 2. Evolve
evolver.fit(
    max_generations=10,
    population_size=50,
    num_islands=4,              # 💎 PRO FEATURE: 4 parallel islands
    timeout_per_eval=60,
    max_poll_time=600,
)

# 3. Inspect
print(f"Best Value:  {evolver.best_fitness_:.6f}")
print(f"Pipeline:    {evolver.best_pipeline_}")
print(f"Evaluations: {evolver.result_.total_evals}")

# 4. Save
evolver.save("optimization_results.json")

Live dashboard — objective value minimization, champion trend, and metaheuristic pipeline leaderboard.

Fitness Configuration

Parameter	Value
Primary Metric	Best function value (lower = better)
Direction	Minimize
Max function evals per candidate	2000
Exception Penalty	+1e9

💎 VEOX Pro Activation

To unlock VIP Evaluators and Pro Algorithms (like PaperKit and Generative routines), you must authenticate your local node with a VEOX License Token.

from veox import VeoxEvolver

evolver = VeoxEvolver("optimization", api_url="http://127.0.0.1:8090")

# 1. Fetch your unique Hardware Fingerprint
fingerprint = evolver.get_system_fingerprint()
print(f"My VEOX Node Fingerprint: {fingerprint}")
# Example Output: My VEOX Node Fingerprint: 476ad03474b31e3c84d07df9088d93f0

# 2. Provide this fingerprint to your VEOX Admin to receive a JWT Token
jwt_token = "eyJ0b2tlbiI6ICJVRExK...EXPIRES"  # Replace with your token

# 3. Activate the Enclave
if evolver.activate_license(jwt_token):
    print("VIP Features Unlocked!")
    # evolver.fit(...) will now utilize full Pro capabilities

Tips

• Server-side benchmarks: Uses 100+ Python objective functions on the server.
• Quick test: max_generations=1, population_size=10 for a smoke test.
• Dimension: Default 10D; the engine configures dimensionality from the benchmark.