LaSER: How Learning Can Guide the Evolution of Equations

Evolution and learning are two distinct yet complementary forms of adaptation. While evolutionary processes operate across generations via the selection of genotypes, learning occurs within the lifetime of an individual, shaping behavior through phenotypic adjustment. The Baldwin effect describes how lifetime learning can improve evolutionary search without altering inherited structures. While this has proven effective in areas like neuroevolution, where gradient-based learning is often used to fine-tune weights or behaviors produced by evolution, it remains underexplored in systems that evolve non-differentiable symbolic structures like Genetic Programming (GP). GP evolves explicit syntax trees that represent equations, offering strong interpretability but limited generalization due to the burden of discovering both useful representations and precise mappings. Here, we show for the first time that integrating a simple form of supervised learning, applied at the semantic or behavioral level during evaluation, can effectively guide the evolution of equations in GP. To achieve this, we propose a new GP pipeline, LaSER (Latent Semantic Evolutionary Regression), where each GP individual generates a semantic representation that is passed to a supervised learner. The quality of the learned mapping is used to assign fitness, without modifying the underlying syntax tree or evolutionary process. Across standard symbolic regression benchmarks, in terms of generalization ability, LaSER significantly outperforms traditional GP and, in several cases, matches or exceeds popular machine learning regressors, while preserving the symbolic interpretability. By separating evolution from learning, LaSER offers a practical route to integrating GP with modern ML workflows, and opens new avenues for research at the intersection of evolutionary computation and representation learning.

Giving Simulated Cells a Voice: Evolving Prompt-to-Intervention Models for Cellular Control

Guiding biological systems toward desired states, such as morphogenetic outcomes, remains a fundamental challenge with far-reaching implications for medicine and synthetic biology. While large language models (LLMs) have enabled natural language as an interface for interpretable control in AI systems, their use as mediators for steering biological or cellular dynamics remains largely unexplored. In this work, we present a functional pipeline that translates natural language prompts into spatial vector fields capable of directing simulated cellular collectives. Our approach combines a large language model with an evolvable neural controller (Prompt-to-Intervention, or P2I), optimized via evolutionary strategies to generate behaviors such as clustering or scattering in a simulated 2D environment. We demonstrate that even with constrained vocabulary and simplified cell models, evolved P2I networks can successfully align cellular dynamics with user-defined goals expressed in plain language. This work offers a complete loop from language input to simulated bioelectric-like intervention to behavioral output, providing a foundation for future systems capable of natural language-driven cellular control.