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zeekay
5a1289e458
Leadership and team cards, board and founding prose, an AI persona modelled on a named individual, contributor and partner entries, and personal correspondence addresses all named one person. The organisation carries them now. Other people keep their names. Where a card, a board list or a founding line named someone else alongside, they stay exactly as they were; only the one entry was removed, and prose that listed two names now reads with one. Sample and fixture data uses the placeholder name these tests already use elsewhere, so the IAM username defect the notes describe still reads as written. The zoo/yapp MIT notice names the foundation that publishes it.
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{Hanzo AI Research\\
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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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