| Author(s) |
Gondro, Cedric
Kinghorn, Brian
Ruvinsky, Anatoly
Gibson, John
|
| Publication Date |
2006
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| Abstract |
Evolutionary Computation encompasses a large group of stochastic problem solving methods loosely inspired on biological evolutionary processes such as selection, mutation and recombination. These methods are commonly referred to as Evolutionary Algorithms and all have in common the use of populations of candidate solutions which reproduce, compete, and are subjected to selective pressures and random variation - the four basic elements of evolution. Some of the best known Evolutionary Algorithms include Genetic Algorithms, Genetic Programming and Evolution Strategies. Evolutionary Algorithms are suited to optimization of complex non linear problems, making them appropriate to optimization of biological problems which are usually complex and non linear. In this thesis Evolutionary Algorithms were developed and used to optimize biological problems.
|
| Link | |
| Title |
Evolutionary Computation for Optimization and Model Discovery in Biological Systems
|
| Type of document |
Thesis Doctoral
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| Entity Type |
Publication
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