Testing the effect of varying environments on the speed of evolution

One of the most important tasks in computer science and artificial intelligence is optimization. Computer scientists use simulation of natural evolution to create algorithms and data structures to solve complex optimization problems. This field of study is called evolutionary computation. In evolutionary computation, the speed of evolution is defined as the number of generations needed for an initially random population to achieve a given goal. Recent studies have shown that varying environments might significantly speed up evolution, and suggested modularly varying goals can accelerate optimization algorithms. In this thesis, we study the effect of varying goals on the speed of evolution. Two test models, the NK model and the midunitation model, are used for this study. Three different evolutionary algorithms are used to test the hypothesis. Statistical analyses of the results showed that under NK model, evolution with fixed goal is faster than evolution with switching goals. Under midunitation model, different algorithms lead to different results. With some string lengths using hill climbing, switching goals sped up evolution. With other string lengths using hill climbing, and using the other evolutionary algorithms, either evolution with a fixed goal was faster or results were inconclusive.

Name thesis:degree_name

Master of Science (MS)

Grantor dc:publisher

University of Montana

Year

2009

Author dc:creator

• Xiao, Zhongmiao

• evolutionary algorithms
• NK landscape
• speed of evolution
• varying environments

Repository record dc:identifier

https://scholarworks.umt.edu/etd/956

OAI identifier oai:identifier

oai:scholarworks.umt.edu:etd-1975