Paper Info
Title
Evolutionary Algorithms based on non-Darwinian theories of evolution
Abstract
One name that comes to mind in connection with the word evolution is Darwin. One evolutionist however, who is rarely talked about, especially in the Artificial Intelligence community, is Peirce. The Darwinian model is based on the concepts of absolute chance, mechanistic laws, and inexplicable interaction between the two. In contrast, Peircepsilas framework posits a dynamic interaction between possibility, necessity and regularity to describe the process of evolution. The theory of evolution proposed by Peirce is superior to the one proposed by Darwin because it is more general and it has greater explanatory power. Peircepsilas insights are significant enough to be used to improve the existing evolutionary algorithms. It was observed during our literature review that almost all evolutionary algorithms are fundamentally based on Darwinian principles of evolution. The present paper highlights the differences between Darwinian and Peircian evolutionary theories and provides the theoretical foundation for developing a novel Peirce based Evolutionary Algorithm. Preliminary experiments have been conducted and results seem very promising.
Year
DOI
Venue
2008
10.1109/CEC.2008.4631141
Evolutionary Computation, 2008. CEC 2008.
Keywords
Field
DocType
evolutionary computation,Darwinian principles of evolution,artificial intelligence,evolutionary algorithms,nonDarwinian theories of evolution
Evolutionary algorithm,Cognitive science,Computer science,Evolutionary computation,Evolution strategy,Darwinian anthropology,Artificial intelligence,Mechanism (philosophy),Genetic algorithm,Machine learning,Evolutionism,Darwinism
Conference
ISBN
Citations 
PageRank 
978-1-4244-1823-7
0
0.34
References 
Authors
6
3
Name
Order
Citations
PageRank
Junaid Akhtar151.49
Mian Awais25911.53
Basit B. Koshul300.34