Automated Search for Artificial Life

• Automated Search for Artificial Life (ASAL) is a framework that uses evolutionary search and multi-objective optimization to autonomously explore life-like behaviors in simulated environments.
• It leverages latent behavioral embeddings and state-of-the-art unsupervised learning to transform complex dynamical patterns into measurable descriptors.
• By integrating foundation models with minimal human feedback, ASAL minimizes bias while promoting open-ended evolution and scalable discovery of emergent phenomena.

Automated Search for Artificial Life (ASAL) is a paradigm for systematically and autonomously discovering, organizing, and quantifying life-like phenomena in computational substrates. By integrating evolutionary search, unsupervised behavioral characterization, multi-objective optimization, and, more recently, foundation models, ASAL enables the continuous exploration of emergent behaviors and scalable mapping of "life as it could be." Techniques under the ASAL umbrella target the challenge of open-ended evolution, seeking to reproduce the unbounded novelty, adaptivity, and complexity of natural evolutionary systems while minimizing human bias and manual intervention.

1. Formalization and Scope of ASAL

At its core, ASAL treats artificial life substrates—such as cellular automata (CA), agent-based systems, morphogenetic fields, and physics-inspired neural environments—as parameterized dynamical systems. Each parameter vector $\theta \in \Theta$ specifies a set of initialization and rule specifications, leading to a trajectory of system states rendered as images, videos, or higher-level descriptors. The search problem is reframed as finding $\theta$ or sets of $\theta$ that maximize intrinsic or extrinsic metrics of interest, characterizing the presence and diversity of life-like structures, agency, and long-term novelty.

The general ASAL pipeline consists of:

1. Substrate Definition: Specification of simulation rules, observation windows, and rendering functions.
2. Behavioral Embedding: Extraction of latent descriptors (via VAEs, analytic features, or foundation models) encoding dynamical and morphological features.
3. Search/Exploration Strategies: Evolutionary algorithms, diversity search, curriculum learning, or optimization in embedding space.
4. Quantification and Archiving: Automated metrics for uniqueness, persistence, stability, and coverage, maintained in searchable archives.
5. Optional Human Feedback: Sparse user interventions to bias exploration toward subjectively valued discovery niches.

Recent developments extend ASAL beyond classical exploration, enabling quantification of open-endedness, automated detection of emergent agency, and the rapid discovery of complex lifeforms, all largely without human curation (Kumar et al., 23 Dec 2024, Hamon et al., 14 Feb 2024, Baid et al., 26 Sep 2025).

2. Behavioral Embeddings and Descriptor Spaces

A foundational element of ASAL is the conversion of complex spatio-temporal patterns into compact, informative descriptors. Early approaches relied on analytic statistical features (mass, oscillation period, spatial moments), while unsupervised learning, notably VAEs and modular latent hierarchies, now prevail.

• Latent Spaces: Patterns $o(\theta)$ are mapped to latent vectors $z = R(o)$, often via a VAE trained on diverse observation archives. Latent codes capture both gross morphology and subtle behavioral traits, enabling population-wise measures such as homeostatic stability, distinctiveness, and sparsity to be computed in a consistent space (Lorantos et al., 3 Jun 2025, Etcheverry et al., 2020).
• Hierarchical/Modular Approaches: The HOLMES architecture dynamically allocates VAE modules and splits embedding space hierarchically, enforcing and measuring meta-diversity across multiple behavioral characterizations. This mitigates observer-bias inherent to monolithic descriptors and supports adaptive exploration toward user preferences (Etcheverry et al., 2020).
• Foundation Model Embeddings: Vision-LLMs (CLIP, DINOv2) provide human-aligned, high-capacity representations: $VLM_\mathrm{img}($render$(\theta))$, capturing both visual and semantic similarities as measured by cosine similarity with text prompts or other rollouts (Kumar et al., 23 Dec 2024, Baid et al., 26 Sep 2025). Similarity in FM space demonstrably approximates human judgment regarding novelty, distinctness, and natural-language correspondence.

3. Exploration, Search, and Optimization Strategies

To circumvent premature convergence and mode-collapse in evolving populations, ASAL leverages multi-faceted search mechanisms:

a. Intrinsic Multi-Objective Exploration:

Instead of optimizing scalarized external rewards, search is structured around Pareto-style dominance in populations using intrinsic objectives:

• Homeostatic Regulation ($f_1$): Penalizes temporal fluctuation in latents $\vec z_i$ over trajectory, enforcing persistence/stability:

$$f_1(x) = -\frac{1}{n} \sum_{i=1}^{n} \|\vec z_i - \bar{\vec z}\|_1$$

• Distinctiveness ($f_2$): Quantifies deviation from the population mean, driving novelty:

$$f_2(x) = \|\bar{\vec z} - \mathbb{E}[\bar{\vec z}]\|_2$$

• Population Sparsity ($f_3$): Rewards placement in underexplored descriptor regions; computed via RBF kernel density in archive $A$:

$$f_3(x) = -\sum_{a \in A} \exp\left(-\frac{\|\vec d(x) - \vec d(a)\|_2^2}{2\sigma^2}\right)$$

Pareto-based ranking (via domination count) preserves trade-offs and maintains a front of solutions balancing stability and exploratory divergence (Lorantos et al., 3 Jun 2025).

b. Diversity-Driven and Curriculum-Based Exploration:

IMGEP (Intrinsic Motivation with Goal Exploration Processes) algorithms maintain an archive of reached behavioral endpoints, sample challenging but achievable new goals in descriptor space, and interleave global novelty sampling with local gradient-based exploitation (fine-tuning $\theta$ for specific outcomes) (Hamon et al., 14 Feb 2024). This curriculum approach accelerates the automatic emergence of agents with robust locomotion and obstacle avoidance.

c. Foundation-Model-Driven Optimization:

Recent ASAL paradigms encode desired phenomena via natural language prompts and utilize pretrained FMs as zero-shot fitness functions. Three main optimization objectives are currently standard (Kumar et al., 23 Dec 2024):

Paradigm	Objective	Description
Supervised	$$θ^* = \arg\max_θ E_T[\langle VLM_\mathrm{img}(RS^T(θ)), VLM_\mathrm{txt}(prompt_T) \rangle]$$	Finds simulations matching textually defined targets
Open-Endedness	$$θ^* = \arg\min_θ E_T [\max_{T'<T} \langle VLM_\mathrm{img}(RS^T(θ)), VLM_\mathrm{img}(RS^{T'}(θ)) \rangle ]$$	Maximizes temporal novelty
Illumination	$$\arg\min_{\theta_0,\ldots,\theta_n} E_{θ,T}[\max_{θ'\neq θ} \langle VLM_\mathrm{img}(RS^T(θ)), VLM_\mathrm{img}(RS^T(θ')) \rangle]$$	Maximizes behavioral repertoire diversity

Optimization algorithms include Sep-CMA-ES, Adam+BPTT (for NCAs), genetic algorithms, and brute-force enumeration for finite rule classes.

4. Scalability, Complexity, and System Design

The high combinatorial complexity of ASAL—particularly the entity/phenomenon detection stage—necessitates attention to both algorithmic and computational efficiency.

• Entity Recognition: Abstracting simulated states as multisets of primitive elements, full submultiset enumeration is NP-hard $(O(2^n))$, but tractable cases exist for non-overlapping partitions (e.g., convex regions in CA) (Misra, 2018).
• Observation and Reproduction Analysis: Entity and population reproduction inference scales exponentially with state-size ($n$), but can be reduced to polynomials via coarse-graining, non-overlap constraints, and event-driven observation.
• Parallel and Distributed Implementation: Large-scale ASAL is enabled by sharding states and archives, employing dynamic graphs and parallel traversal (e.g., BFS-frontier techniques), and early-exit heuristics during lineage or novelty searches.
• System Recommendations: Minimize effective $n$ through hierarchical state representations, bound search via mutation thresholds, adopt incremental updates, and utilize hash-based or low-dimensional signature for similarity tests (Misra, 2018).

5. Role of Foundation Models and Human Alignment

The integration of vision-language and vision-only FMs represents a decisive shift. By providing general, human-aligned embedding spaces, FMs allow ASAL systems to:

• Quantify and compare outputs in terms that closely approximate human judgments of “novelty,” “interestingness,” and even semantic prompt satisfaction.
• Automate the search for not just unprecedented forms, but for those meeting arbitrary high-level goals or reflecting open-ended evolutionary narratives.
• Enable illumination-style searches that construct atlases of distinct lifeforms (e.g., Lenia organisms, Boids patterns) and enable precise mapping of qualitative thresholds (“more is different” effects).

A plausible implication is a decoupling of ASAL from brittle, ad hoc metrics, in favor of universally comparable, data-driven measures of life-likeness (Kumar et al., 23 Dec 2024, Baid et al., 26 Sep 2025).

6. Empirical Results and Impact

Experimental studies across cellular automata, Lenia, Boids, morphogenetic systems, and self-organizing neural substrates have demonstrated that ASAL:

• Systematically discovers thousands of structurally and behaviorally distinct artificial organisms, often with properties such as robust self-maintenance, spontaneous motility, sensorimotor agency, and collective coherence (Hamon et al., 14 Feb 2024, Kumar et al., 23 Dec 2024).
• Outperforms hand-crafted search and baselines in terms of coverage of behavioral descriptor space, diversity, and robustness to environmental perturbation.
• Produces qualitative transitions (multi-species coexistence, predator-prey oscillations, division of labor), and discovers “synthetic” open-ended cellular automata rules on par with Life.
• Adapts rapidly to user preference via sparse feedback in modular architectures (meta-diversity), rapidly shifting exploratory focus with minimal human steering (Etcheverry et al., 2020).
• Reveals, via FM-based quantification, that measured “diversity” or open-endedness is sensitive to choice of embedding and alignment method; different FM choices induce different effective search frontiers (Kumar et al., 23 Dec 2024).

7. Limitations, Open Problems, and Future Directions

Despite these achievements, several limitations persist:

• Intrinsic objectives and foundation models encode observer-dependent bias; coverage and “interestingness” are fundamentally conditional on the embedding and search schedule.
• Computational cost remains high, particularly when backpropagating through large simulations or when brute-force enumeration of rule spaces is required.
• While Pareto-based and non-scalarized selection foster trade-off preservation, they can slow convergence or exploration in ultra-high-dimensional substrates.

Emerging research aims to:

• Co-evolve or meta-learn the behavioral descriptors and observation functions, automating the discovery of relevant behavioral order parameters.
• Integrate foundation models capable of video understanding, richer semantic alignment, or multi-agent interaction description.
• Deploy “open-endedness engines” (e.g. ASAL++) that extend search via FM-proposed evolutionary targets, creating synthetic phylogenies and exploring evolutionary narratives with minimal human intervention (Baid et al., 26 Sep 2025).
• Generalize beyond spatial simulations (e.g., networked dynamical systems, reaction networks) and incorporate physically plausible, multi-modal sensors/effectors.

A plausible implication is that as FM capacity and interpretability increase—and as ASAL systems are scaled to exascale simulation clusters—fully automated, continually expanding discovery of artificial life morphologies, behaviors, and higher-level ecologies will become routine, offering new empirical ground for both artificial and natural life sciences.