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Erschienen in:
Application of Machine-Learning Methods to Understand Gene Expression Regulation Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

9. Analyzing a Decade of Human-Competitive (“HUMIE”) Winners: What Can We Learn?

verfasst von : Karthik Kannappan, Lee Spector, Moshe Sipper, Thomas Helmuth, William La Cava, Jake Wisdom, Omri Bernstein

Erschienen in: Genetic Programming Theory and Practice XII

Verlag: Springer International Publishing

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Abstract

Techniques in evolutionary computation (EC) have improved significantly over the years, leading to a substantial increase in the complexity of problems that can be solved by EC-based approaches. The HUMIES awards at the Genetic and Evolutionary Computation Conference are designed to recognize work that has not just solved some problem via techniques from evolutionary computation, but has produced a solution that is demonstrably human-competitive. In this chapter, we take a look across the winners of the past 10 years of the HUMIES awards, and analyze them to determine whether there are specific approaches that consistently show up in the HUMIE winners. We believe that this analysis may lead to interesting insights regarding prospects and strategies for producing further human competitive results.

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Vorheriges Kapitel How to Exploit Alignment in the Error Space: Two Different GP Models
Nächstes Kapitel Tackling the Boolean Multiplexer Function Using a Highly Distributed Genetic Programming System
Fußnoten
1
see sigevo.org
 
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Metadaten
Titel
Analyzing a Decade of Human-Competitive (“HUMIE”) Winners: What Can We Learn?
verfasst von
Karthik Kannappan
Lee Spector
Moshe Sipper
Thomas Helmuth
William La Cava
Jake Wisdom
Omri Bernstein
Copyright-Jahr
2015
Buch
Genetic Programming Theory and Practice XII

Print ISBN: 978-3-319-16029-0

Electronic ISBN: 978-3-319-16030-6

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-16030-6_9

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