DocumentCode
2917078
Title
The Epigenetic Algorithm
Author
Periyasamy, Sathish ; Gray, Alex ; Kille, Peter
Author_Institution
Cardiff Sch. of Comput. Sci., Cardiff Univ., Cardiff
fYear
2008
fDate
1-6 June 2008
Firstpage
3228
Lastpage
3236
Abstract
Evolutionary computation (EC) paradigms are inspired by the optimization strategies utilized by biological systems. While these strategies can be found in every level of biological organization, almost all of the EC techniques which comprise techniques from evolutionary algorithm (EA) to swarm intelligence (SI) have been inspired by organism level optimization strategies. While EA is based on trans-generational genetic adaptation of organisms (biologically inspired), SI is mainly based on intra-generational collective behavioral adaptation of organisms (socially inspired). This paper describes the optimization strategies that bio-molecules utilize both for intra-generational and trans-generational adaptation of biological cells. These adaptive strategies which are known as epigenetic mechanisms emerged long before any other biological strategy and form the basis for Epigenetic algorithms (EGA). Further, the paper proposes an intra-generational EGA based on bio-molecular degradation and autocatalysis which are ubiquitous cellular processes and are pivotal for the adaptive dynamics and evolution of intelligent cellular organization.
Keywords
biology computing; cellular biophysics; evolutionary computation; optimisation; ubiquitous computing; autocatalysis; behavioral adaptation; biological cells; biological organization; biomolecules; evolutionary algorithm; evolutionary computation; optimization strategies; organism level optimization strategies; swarm intelligence; transgenerational adaptation; transgenerational genetic adaptation; ubiquitous cellular processes; Evolutionary computation;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-1822-0
Electronic_ISBN
978-1-4244-1823-7
Type
conf
DOI
10.1109/CEC.2008.4631235
Filename
4631235
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