NDLI: Darwin, Lamarck, or Baldwin: Applying Evolutionary Algorithms to Machine Learning Techniques

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WI 2014 Non-Program Committee Reviewers - II

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Darwin, Lamarck, or Baldwin: Applying Evolutionary Algorithms to Machine Learning Techniques

Spammer Classification Using Ensemble Methods over Structural Social Network Features

FoP: Never-Ending Learner for Multimedia Knowledge Extraction

A Clique Based Web Page Classification Corrective Approach

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WI 2014 - II Author Index

WI 2014 - II Publisher's Information

Darwin, Lamarck, or Baldwin: Applying Evolutionary Algorithms to Machine Learning Techniques

Content Provider	ACM Digital Library
Author	Holzinger, Katharina Holzinger, Andreas Palade, Vasile Rabadan, Raul Bloice, Marcus Blanchard, David
Abstract	Evolutionary Algorithms (EAs), inspired by biological mechanisms observed in nature, such as selection and genetic changes, have much potential to find the best solution for a given optimisation problem. Contrary to Darwin, and according to Lamarck and Baldwin, organisms in natural systems learn to adapt over their lifetime and allow to adjust over generations. Whereas earlier research was rather reserved, more recent research underpinned by the work of Lamarck and Baldwin, finds that these theories have much potential, particularly in upcoming fields such as epigenetics. In this paper, we report on some experiments with different evolutionary algorithms with the purpose to improve the accuracy of data mining methods. We explore whether and to what extent an optimisation goal can be reached through a calculation of certain parameters or attribute weightings by use of such evolutionary strategies. We provide a look at different EAs inspired by the theories of Darwin, Lamarck, and Baldwin, as well as the problem solving methods of certain species. In this paper we demonstrate that the modification of well-established machine learning techniques can be achieved in order to include methods from genetic algorithm theory without extensive programming effort. Our results pave the way for much further research at the cross section of machine learning optimisation techniques and evolutionary algorithm research.
Starting Page	449
Ending Page	453
Page Count	5
File Format	PDF
ISBN	9781479941438
DOI	10.1109/WI-IAT.2014.132
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-11
Access Restriction	Subscribed
Subject Keyword	Evolutionary algorithms machine learning data mining optimization
Content Type	Text
Resource Type	Article

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