arXiv (All)
open access
Relocating Nonlinearity: How a Downstream Learner Reshapes What Genetic Programming Must Evolve
Genetic programming was conceived as a way of evolving solutions: the program is the answer, and fitness is the error of its own output. A substantial line of work instead makes the program an input to a separate learner, so fitness measures the learner's output rather than the program's. Which lear
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- HERO-MoE: Historical Expert Routing with Scale-Preserving Fusion#1116912
- Rethinking Learnability in Offline Data-driven Optimization#627685
- Rethinking Learnability in Offline Data-driven Optimization#822048
- KLineage: Recovering the Missing When of Kernel Optimization by Deoptimizing Experts#1141552
- The dynamics of a genetic algorithm for a simple learning problem#234224
Record · ID 1233386
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive