Cognitive-Enhanced Evolutionary Framework

• Cognitive-enhanced evolutionary frameworks integrate evolutionary principles with cognitive components such as memory, attention, and abstraction to enable continual adaptation in complex systems.
• They combine bottom-up sensory processing with top-down reasoning to balance exploration and consolidation through dynamic operational memory.
• These frameworks yield improved generalization, efficiency, and robustness in AGI by evolving adaptive models across developmental phases.

A cognitive-enhanced evolutionary framework constitutes an overview of evolutionary principles and cognitive mechanisms to achieve continual, generalizable, and efficient adaptation in artificial and natural learning systems. Rather than relying on random variation and simple fitness-based selection alone, these frameworks integrate memory, abstraction, attention, reasoning, and purpose-driven organization to enable the emergence, consolidation, and propagation of adaptive models, strategies, and behaviors. Variants apply both in computational models of artificial general intelligence (AGI) and in simulating cultural, biological, and societal evolution, often yielding superior efficiency, robustness, and open-ended capability relative to naive selectionist or traditional evolutionary algorithms.

1. System Architecture and Core Components

Cognitive-enhanced evolutionary systems typically combine layered representations and interactive subsystems to structure both learning and evolution. For example, the “Model Of Models” (MOM) architecture for AGI (Komarovsky, 2023) encompasses:

• Representation Layers:
  • Level 0 (S-sensory, sub-symbolic): raw environmental inputs (vision, audition, proprioception).
  • Level 1 (Instance Layer): objects, actions, and relations, each with attributes and operations.
  • Level 2 (Class/Model Layer): abstract frames or classes constructed over sets of instances.
• Subsystems:

1. Operational Memory (OM): A dynamic semantic graph encoding current instances, actions, and relations, annotated by usage counts (visits), uncertainty, and consolidation status.
2. Will & Attention Module: Maintains agent drives (vector $w \in \mathbb{R}^d$) and selectively focuses attention ($\alpha_t$) on a small active model subset.
3. Modeling/Abstraction Engine: Enables bottom-up induction (cluster formation) and top-down deduction (instance projection from classes).
4. Consolidation Controller: Governs online fast updates and offline global consolidation (pruning, merging, abstraction formation).

Interaction proceeds via cyclic sensing, bottom-up detail lifting, top-down biasing, local learning, and periodic offline consolidation.

2. Duality Principle and Learning Dynamics

A defining aspect is the duality of bottom-up and top-down learning, which governs the flow of information and adjustment of representations:

• Bottom-Up (BU) Induction: Effects observational learning at the instance level; weights $w_i$ are updated by:

$$w_i^{(t+1)} = w_i^{(t)} + \eta_{\mathrm{bu}}\,\delta_i^{\mathrm{obs}}\,x_i$$

where $x_i$ is the activating feature and $\delta_i^{\mathrm{obs}}$ the induction error.

• Top-Down (TD) Deduction: Projects constraints or prototypes from classes onto instances:

$$w_i^{(t+1)} = w_i^{(t)} + \eta_{\mathrm{td}}\, \bigl(f_{\mathrm{class}(C)} - h_i\bigr)$$

with $f_{\mathrm{class}(C)}$ the desired class-prototype and $h_i$ the current instance representation.

• Combined Update: Superposes DU/TDE increments, with relative gains ($\eta_{\mathrm{bu}}, \eta_{\mathrm{td}}$) shifting over developmental time.

This bidirectional process prevents over-specialization and dogmatic bias, fostering robust pattern extraction and flexible abstraction.

3. Evolutionary Timeline and Consolidation Principle

Cognitive-evolutionary systems traverse developmental phases marked by characteristic consolidation and model dynamics:

• Infancy ($t < t_0$): Exploration dominates; learning rates balanced; high stochasticity and minimal consolidation ($\epsilon \approx 1$, low $C_t(v)$).
• Adolescence ($t \approx t_0$): Top-down bias strengthens; overlap in models triggers merging; exploration shrinks ($\epsilon \rightarrow 0.1$), consolidation increases.
• Adulthood ($t \gg t_0$): Saturation at maximum abstraction cardinality ($N_{\max}$), memory footprint stabilized, high recall efficiency.

Mathematically, model-count growth is described by logistic form: $$N(t) = \frac{N_{\max}}{1 + \exp\bigl(-k(t-t_0)\bigr)}$$ and system cost by: $$C(t) = \alpha\,N(t) + \beta\,\mathcal{C}_{\mathrm{mem}}(t)$$ with offline consolidation reducing $\mathcal{C}_{\mathrm{mem}}$ over time.

4. Operational Memory: Structure and Update Mechanisms

Dynamic operational memory is a semantic graph $G_t = (V_t, E_t)$ with vertices $V_t = \{O_t, A_t, R_t\}$ (objects, actions, relations). Updates occur in phases:

• Online Update (activated subgraph $S_t$):

$$C_t(v) \leftarrow C_t(v) + \gamma [1 - C_t(v)], \qquad \text{visits}(v) \gets \text{visits}(v) + 1$$

• Offline Consolidation:
  • Merge any pair $(v_i, v_j)$ with high similarity ($\geq \theta_n$),
  • Prune weak edges ($\mathrm{support} < \theta_e$),
  • Promote robust subgraphs as new abstractions.

OM ensures continual adaptation, memory economy, and structured generalization.

5. Algorithms and Evolutionary Workflow

Canonical cognitive-evolutionary workflows decompose into cyclic stages, integrating sensory data, attention, dual learning, and consolidation:

• Model Creation (online):

for each timestep t: senses ← read_inputs() candidates ← bottom_up_match(senses, OM) selected ← attention(will, candidates) Δlocal ← BU_update(selected, senses) + TD_update(selected, will) OM ← apply_updates(OM, selected, Δlocal) if consolidation_condition: OM ← consolidate(OM) # offline step

• Retuning Existing Models:

function adapt_model(M_i, data): α ← attention_score(M_i, will) Δw_BU ← η_bu * ∂Loss_BU(M_i, data)/∂w Δw_TD ← η_td * ∂Loss_TD(M_i, will)/∂w M_i.weights += Δw_BU + Δw_TD return M_i

• Generalization vs. Specialization:

if new data consistent with M_i across many contexts: specialize(M_i, data_subset) else: generalize(M_new, {M_i, data})

These routines orchestrate the incremental refinement and consolidation of operational models. Key parameters ($$\eta_{\mathrm{bu}}, \eta_{\mathrm{td}}, \epsilon(t), \theta_n, \theta_e, N_{\max}, k, t_0$$) modulate learning rates, exploration, and structural thresholds.

6. Expected Performance and Functional Implications

Cognitive-enhanced evolutionary frameworks deliver distinct advantages over non-cognitive evolutionary systems:

• Generalization: Early network expansion captures universal patterns, enabling broad applicability.
• Specialization: Frequent instance visitation sharpens individual model fidelity.
• Efficiency: Progressive consolidation minimizes both mnemonic and computational cost; attention restricts updates to active subgraphs ($O(1)$ per step).
• Robustness: Alternating induction and deduction buffers against both noise and premature abstraction.
• Stability: Repeated consolidation cycles ensure persistence and reusability of robust models.

This architecture yields a dynamic operational memory of models and instances, characterized by a transition from exploratory infancy to efficient, abstraction-rich adulthood, anchored by purposeful drives and an integrative approach to learning and adaptation (Komarovsky, 2023).

7. Broader Context and Research Directions

Cognitive-enhanced evolutionary principles are fundamental to the design of AGI agents and autonomous cognitive systems. The MOM framework generalizes beyond the AKREM and DENN traditions by merging operational modeling, dual-process learning, and consolidation into a holistic developmental paradigm. Its emphasis on purpose-driven cognition, reusability, and simplicity provides both theoretical rigor and practical efficiency. Central challenges include scaling to high-dimensional domains, achieving optimal consolidation schedules, and extending beyond model-centric representations to encompass richer forms of will and attention.

Emerging research continues to investigate how layered operational memories, principled consolidation, and bidirectional inductive–deductive learning mechanisms can be adapted in multiscale evolutionary contexts, with applications in AGI, cultural evolution, and domain-agnostic continual learning systems.