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1990two/
evolutionary_turing

pytorch

evolutionary-algorithms

neural-turing-machines

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Evolutionary Turing Machine - The Classics Revival

Neural Turing Machines That Evolve Their Own Architectures

Experimental Research Code - Functional but unoptimized, expect rough edges

What Is This?

Evolutionary Turing Machine combines Neural Turing Machines with evolutionary algorithms to create memory-augmented networks that evolve their own architectures. Instead of hand-designing memory systems, populations of NTMs compete and evolve optimal memory configurations.

Core Innovation: NTMs that mutate their memory slots, read/write heads, and controller architectures through evolutionary pressure, discovering novel memory patterns.

Architecture Highlights

Self-Evolving Memory: Memory slots and dimensions adapt through evolution
Adaptive Read/Write Heads: Number and behavior of memory heads evolve
Controller Architecture Search: LSTM controller dimensions discovered evolutionarily
Task-Driven Fitness: Evolution guided by performance on memory tasks
Population Diversity: Maintains genetic diversity in memory architectures
Crossover Operations: Recombines successful memory strategies

Quick Start

python

from evolutionary_turing import EvolutionaryTuringMachine, EvolutionaryTuringConfig

# Create evolutionary NTM system
config = EvolutionaryTuringConfig(
    population_size=50,
    input_dim=8,
    output_dim=8,
    max_generations=100
)

evolution = EvolutionaryTuringMachine(config)

# Evolve population on memory tasks
history = evolution.run_evolution()

# Get the best evolved model
best_ntm = evolution.get_best_model()

Current Status

Working: Population evolution, architecture mutations, memory task evaluation, crossover operations
Rough Edges: No distributed evolution, limited task variety, basic fitness functions
Still Missing: Advanced mutation operators, multi-objective optimization, neural architecture search integration
Performance: Functional on toy problems, needs scaling for complex tasks
Memory Usage: High due to population storage, optimization needed
Speed: Sequential evaluation, parallelization would help significantly

Mathematical Foundation

The evolutionary process optimizes the NTM architecture space through genetic algorithms:

Fitness(NTM) = Performance(copy_task) × 0.5 + Performance(recall_task) × 0.3 + Efficiency × 0.2

Mutations modify:

Controller dimensions: d_new = d_old + N(0, σ_controller)
Memory parameters: M_slots_new ~ U[16, 256], M_dim_new ~ U[8, 64]
Head configurations: heads_read_new ~ U[1, 4], heads_write_new ~ U[1, 3]

Selection pressure favors architectures that balance task performance with computational efficiency.

Research Applications

Neural architecture search for memory systems
Adaptive memory allocation strategies
Meta-learning through evolutionary computation
Automated machine learning for sequence tasks
Evolutionary neural network design

Installation

bash

pip install torch numpy
# Download evolutionary_turing.py from this repo

The Classics Revival Collection

Evolutionary Turing Machine is part of a larger exploration of foundational algorithms enhanced with modern neural techniques:

Evolutionary Turing Machine ← You are here
Hebbian Bloom Filter
Hopfield Decision Graph
Liquid Bayes Chain
Liquid State Space Model
Möbius Markov Chain
Memory Forest

Citation

bibtex

@misc{evolutionaryturing2025,
  title={Evolutionary Turing Machine: Self-Evolving Memory Architectures},
  author={Jae Parker 𓅸 1990two},
  year={2025},
  note={Part of The Classics Revival Collection}
}

1990two/evolutionary_turing