mirror of
https://github.com/hanzoai/papers.git
synced 2026-11-03 02:27:30 +00:00
zeekay
0d1d121451
Fix shared/ include paths to ../shared/ for 55 subdirectory papers so
that hanzocover.sty and lstlang.tex resolve when building from each
paper's own directory (the build pattern used by latexmk per paper).
Build fixes:
- hanzo-operative/sections/03-screen-understanding-pipeline.tex: move
\COMMENT from after \FOR{...} onto the \STATE line so the algorithmic
env doesn't mis-parse.
- hanzo-network-architecture (abstract + sections 04, 12, 13): escape
unicode mu (U+03BC) as $\mu$ for pdflatex.
- ntt-transform/sections/08-benchmark-results.tex: same mu escape.
All 55 Tier-2/3 papers now build clean with latexmk -pdf.
52 lines
2.0 KiB
TeX
52 lines
2.0 KiB
TeX
\documentclass[11pt,twocolumn]{article}
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\usepackage{../shared/hanzocover}
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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\usepackage{amsmath,amssymb}
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\usepackage{graphicx}
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\usepackage{hyperref}
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\usepackage{cite}
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\usepackage{algorithm}
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\usepackage{algorithmic}
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\usepackage{listings}
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\input{../shared/lstlang}
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\usepackage{xcolor}
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\lstset{
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basicstyle=\ttfamily\small,
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keywordstyle=\color{blue},
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commentstyle=\color{gray},
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stringstyle=\color{red},
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breaklines=true,
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frame=single
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}
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\title{Earle: Genetic Algorithms for Automated Marketing Optimization}
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\author{Zach Kelling\\
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Hanzo Industries\\
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\texttt{[email protected]}}
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\date{March 2016}
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\begin{document}
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\hanzocoverpage
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\begin{abstract}
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We present Earle, a system for automated marketing optimization using genetic algorithms. Traditional A/B testing evaluates a small number of variants sequentially, limiting exploration of the combinatorial space of marketing parameters. Earle treats marketing campaigns as genomes—structured combinations of headlines, images, copy, and targeting criteria—and evolves populations of campaigns toward conversion objectives. Our approach enables simultaneous exploration of thousands of variants while automatically allocating budget toward high-performing individuals. We formalize the marketing optimization problem, describe our genetic algorithm implementation with domain-specific operators, and evaluate Earle on production campaigns across e-commerce, SaaS, and lead generation verticals. Results demonstrate 34\% average improvement in conversion rates compared to human-designed campaigns and 2.8$\times$ faster convergence than multi-armed bandit approaches.
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\end{abstract}
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\input{sections/01-introduction}
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\input{sections/02-background}
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\input{sections/03-problem-formulation}
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\input{sections/04-algorithm-design}
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\input{sections/05-implementation}
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\input{sections/06-evaluation}
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\input{sections/07-discussion}
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\input{sections/08-related-work}
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\input{sections/99-conclusion}
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\end{document}
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