GeM-EA: A Generative and Meta-learning Enhanced Evolutionary Algorithm for Streaming Data-Driven Optimization explores An evolutionary algorithm enhanced with meta-learning and generative replay for robust and fast optimization of streaming data with concept drift.. Commercial viability score: 7/10 in Optimization AI.
Use This Via API or MCP
This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Page Freshness
Canonical route: /paper/gem-ea-a-generative-and-meta-learning-enhanced-evolutionary-algorithm-for-streaming-data-driven-optimization
Page-specific freshness sourced from this paper's evidence receipt and score bundle.
Agent Handoff
Canonical ID gem-ea-a-generative-and-meta-learning-enhanced-evolutionary-algorithm-for-streaming-data-driven-optimization | Route /paper/gem-ea-a-generative-and-meta-learning-enhanced-evolutionary-algorithm-for-streaming-data-driven-optimization
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/gem-ea-a-generative-and-meta-learning-enhanced-evolutionary-algorithm-for-streaming-data-driven-optimizationMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.12336"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "GeM-EA: A Generative and Meta-learning Enhanced Evolutionary Algorithm for Streaming Data-Driven Optimization",
"normalized_query": "2604.12336",
"route": "/paper/gem-ea-a-generative-and-meta-learning-enhanced-evolutionary-algorithm-for-streaming-data-driven-optimization",
"paper_ref": "gem-ea-a-generative-and-meta-learning-enhanced-evolutionary-algorithm-for-streaming-data-driven-optimization",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
Research neighborhood
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Dimensions overall score 7.0
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