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A Prescriptive Formalism for Constructing Domain-specific Evolutionary Algorithms (1998)
| Citations: | 14 - 0 self |
BibTeX
@TECHREPORT{Surry98aprescriptive,
author = {Patrick David Surry},
title = {A Prescriptive Formalism for Constructing Domain-specific Evolutionary Algorithms},
institution = {},
year = {1998}
}
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Abstract
It has been widely recognised in the computational intelligence and machine learning communities that the key to understanding the behaviour of learning algorithms is to understand what representation is employed to capture and manipulate knowledge acquired during the learning process. However, traditional evolutionary algorithms have tended to employ a fixed representation space (binary strings), in order to allow the use of standardised genetic operators. This approach leads to complications for many problem domains, as it forces a somewhat artificial mapping between the problem variables and the canonical binary representation, especially when there are dependencies between problem variables (e.g. problems naturally defined over permutations). This often obscures the relationship between genetic structure and problem features, making it difficult to understand the actions of the standard genetic operators with reference to problem-specific structures. This thesis instead advocates m...
Keyphrases
prescriptive formalism domain-specific evolutionary algorithm problem variable learning process binary string standardised genetic operator machine learning community problem feature many problem domain problem-specific structure artificial mapping canonical binary representation fixed representation space standard genetic operator genetic structure computational intelligence traditional evolutionary algorithm
