Analyzing a Decade of Human-Competitive ("HUMIE") Winners: What Can We Learn?

Karthik Kannappan; Lee Spector; Moshe Sipper; Thomas Helmuth; William G. La Cava; Jake Wisdom; Omri Bernstein

doi:10.1007/978-3-319-16030-6_9

Analyzing a Decade of Human-Competitive ("HUMIE") Winners: What Can We Learn?

Karthik Kannappan
, Lee Spector
, Moshe Sipper
, Thomas Helmuth
, William G. La Cava
, Jake Wisdom
, Omri Bernstein

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Genetic Programming Theory and Practice XII
Editors	R. Riolo, W. Worzel, M. Kotanchek
Pages	149-166
ISBN (Electronic)	978-3-319-16030-6
DOIs	https://doi.org/10.1007/978-3-319-16030-6_9
State	Published - 2015

Keywords

HUMIES
Evolutionary Computation
Human Competitive

Access to Document

10.1007/978-3-319-16030-6_9

Cite this

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title = "Analyzing a Decade of Human-Competitive ({"}HUMIE{"}) Winners: What Can We Learn?",

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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doi = "10.1007/978-3-319-16030-6\_9",

language = "English",

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AU - Helmuth, Thomas

AU - Cava, William G. La

AU - Wisdom, Jake

AU - Bernstein, Omri

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - HUMIES

KW - Evolutionary Computation

KW - Human Competitive

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M3 - Conference contribution

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EP - 166

BT - Genetic Programming Theory and Practice XII

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A2 - Worzel, W.

A2 - Kotanchek , M.

ER -

Analyzing a Decade of Human-Competitive ("HUMIE") Winners: What Can We Learn?

Abstract

Keywords

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