Adaptation and Self-Organization in Evolutionary Algorithms
James M Whitacre
TL;DR
This paper explores how adaptation and self-organization in evolutionary algorithms can enhance their performance, deepen understanding of natural evolution, and incorporate complex biological mechanisms.
Contribution
It introduces new mechanisms for mimicking biological systems and analyzes their impact on the effectiveness of evolutionary algorithms.
Findings
Improved performance of EAs through adaptation mechanisms
Enhanced understanding of natural evolution processes
Incorporation of complex biological features into EAs
Abstract
Abbreviated Abstract: The objective of Evolutionary Computation is to solve practical problems (e.g. optimization, data mining) by simulating the mechanisms of natural evolution. This thesis addresses several topics related to adaptation and self-organization in evolving systems with the overall aims of improving the performance of Evolutionary Algorithms (EA), understanding its relation to natural evolution, and incorporating new mechanisms for mimicking complex biological systems.
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