Masters Theses

Keywords and Phrases

Evolutionary algorithms; Genetic programming; Hyper-heuristics


"Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population of individuals, by regulating the probability that an individual's genes survive, typically based on fitness. Various conventional fitness based selection functions exist, each providing a unique method of selecting individuals based on their fitness, fitness ranking within the population, and/or various other factors. However, the full space of selection algorithms is only limited by max algorithm size, and each possible selection algorithm is optimal for some EA configuration applied to a particular problem class. Therefore, improved performance is likely to be obtained by tuning an EA's selection algorithm to the problem at hand, rather than employing a conventional selection function. This thesis details an investigation of the extent to which performance can be improved by tuning the selection algorithm. We do this by employing a Hyper-heuristic to explore the space of algorithms which determine the methods used to select individuals from the population. We show, with both a conventional EA and a Covariance Matrix Adaptation Evolutionary Strategy, the increase in performance obtained with a tuned selection algorithm, versus conventional selection functions. Specifically, we measure performance on instances from several benchmark problem classes, including separate testing instances to show generalization of the improved performance. This thesis consists of work that was presented at the Genetic and Evolutionary Computation Conference (GECCO) in 2018, as well as work that will be submitted to GECCO in 2019"--Abstract, page iii.


Tauritz, Daniel R.

Committee Member(s)

Taylor, Patrick
Mulder, Samuel


Computer Science

Degree Name

M.S. in Computer Science


Missouri University of Science and Technology

Publication Date

Spring 2019


viii, 57 pages

Note about bibliography

Includes bibliographical references (pages 54-56).


© 2019 Samuel Nathan Richter, All rights reserved.

Document Type

Thesis - Open Access

File Type




Thesis Number

T 11549

Electronic OCLC #