Supplemental Material: Base Diagnostics
2023-08-30
Chapter 1 Introduction
This is the supplemental material for experiments breaking down nondominated sorting into its two main components: phenotypic fitness sharing and nondominated front ranking. We evaluated these components, along with standard nondominated sorting, on the contradictory objectives diagnostic to measure their contribution on the overall effectiveness of nondominated sorting.
1.1 About our supplemental material
This supplemental material is hosted on GitHub using GitHub pages. The source code and configuration files used to generate this supplemental material can be found in this GitHub repository. We compiled our data analyses and supplemental documentation into this nifty web-accessible book using bookdown.
Our supplemental material includes the following paper figures and statistics:
- Nondomintaed sorting breakdown (Section ??)
1.3 Computer Setup
These analyses were conducted in the following computing environment:
## _
## platform x86_64-pc-linux-gnu
## arch x86_64
## os linux-gnu
## system x86_64, linux-gnu
## status
## major 4
## minor 3.1
## year 2023
## month 06
## day 16
## svn rev 84548
## language R
## version.string R version 4.3.1 (2023-06-16)
## nickname Beagle Scouts
1.4 Experimental setup
Setting up required variables variables.
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## Loading required package: rlang
# data diractory for gh-pages
DATA_DIR = '/opt/ECJ-2023-Suite-Of-Diagnostic-Metrics-For-Characterizing-Selection-Schemes/DATA/CONTRADICTORY_NONDOMINATED/'
# data diractory for local testing
# DATA_DIR = '~/Desktop/Repositories/ECJ-2023-Suite-Of-Diagnostic-Metrics-For-Characterizing-Selection-Schemes/DATA/CONTRADICTORY_NONDOMINATED/'
# graph variables
SHAPE = c(5,3,1)
cb_palette <- c('#88CCEE','#EE7733','#EE3377')
p_theme <- theme(
plot.title = element_text( face = "bold", size = 20, hjust=0.5),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
legend.title=element_text(size=18, hjust = 0.5),
legend.text=element_text(size=10),
axis.title = element_text(size=18),
axis.text = element_text(size=16),
legend.position="bottom",
legend.margin = margin(0, 0, 0, 0),
panel.background = element_rect(fill = "#f1f2f5",
colour = "white",
linewidth = 0.5, linetype = "solid")
)
# default variables
DIMENSIONALITY = 100
GENERATIONS = 50000
# selection scheme related stuff
ACRO = c('nds','nfr','pfs')
NAMES = c('Nondominated sorting (nds)','Nondominated front ranking (nfr)','Phenotypic fitness sharing (pfs)')