Recent New Development in Evolutionary Programming

Xin YAO

doi:10.1007/978-1-4471-0819-1_4

Recent New Development in Evolutionary Programming

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

Abstract

Evolutionary programming (EP) is one of the major branches of evolutionary computation. It has been applied to many learning and optimisation problems with success in recent years. This paper gives an overview of the latest results on evolutionary programming. In particular, the paper will analyse why the recently proposed fast EP performs better than classical EP for most benchmark functions, discuss the scalability of EP, and demonstrate how EP has been used to solve the function optimisation problem and neural network design problem.

Original language	English
Title of host publication	Advances in Soft Computing: Engineering Design and Manufacturing
Editors	Rajkumar ROY, Takeshi FURUHASHI, Pravir K. CHAWDHRY
Publisher	Springer London
Pages	30-56
Number of pages	27
ISBN (Electronic)	9781447108191
ISBN (Print)	9781852330620
DOIs	https://doi.org/10.1007/978-1-4471-0819-1_4
Publication status	Published - 1999
Externally published	Yes

Keywords

Evolutionary programming
global optimisation
evolutionary learning
neural networks
mutation

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10.1007/978-1-4471-0819-1_4

Cite this

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title = "Recent New Development in Evolutionary Programming",

abstract = "Evolutionary programming (EP) is one of the major branches of evolutionary computation. It has been applied to many learning and optimisation problems with success in recent years. This paper gives an overview of the latest results on evolutionary programming. In particular, the paper will analyse why the recently proposed fast EP performs better than classical EP for most benchmark functions, discuss the scalability of EP, and demonstrate how EP has been used to solve the function optimisation problem and neural network design problem.",

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AB - Evolutionary programming (EP) is one of the major branches of evolutionary computation. It has been applied to many learning and optimisation problems with success in recent years. This paper gives an overview of the latest results on evolutionary programming. In particular, the paper will analyse why the recently proposed fast EP performs better than classical EP for most benchmark functions, discuss the scalability of EP, and demonstrate how EP has been used to solve the function optimisation problem and neural network design problem.

KW - Evolutionary programming

KW - global optimisation

KW - evolutionary learning

KW - neural networks

KW - mutation

U2 - 10.1007/978-1-4471-0819-1_4

DO - 10.1007/978-1-4471-0819-1_4

M3 - Conference paper (refereed)

SN - 9781852330620

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EP - 56

BT - Advances in Soft Computing: Engineering Design and Manufacturing

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A2 - FURUHASHI, Takeshi

A2 - CHAWDHRY, Pravir K.

PB - Springer London

ER -

Recent New Development in Evolutionary Programming

Abstract

Keywords

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