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Wikipedia on Evolutionary Art

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XE6 Vector Essentials - September 26, 2015

The essential vector operations for evolutionary recombination are included in a small application which operates on a sequence of choices made for each of a number of items. These operations are visualiaed as numeric data and simultaneously as colors and musical notes using colorRamp() and midiRamp(). In addition, a sequence of rests or intervals, which are set by the user, modify the tempo of the music. A sample tune and rhythm play when the mouse enters the window.

XE6 EvoCrypt - Adaptation by Trey Bagley - 21 April 2015

Enter a cryptotext (you can input the one that appears by default), open the two dictionaries and begin the run(s). 100 runs of 100 generations will complete in one minute. 100 runs of 1000 generations will complete in 17 minutes. You can help this evolutionary algorithm by locking in specific Cryptext to Cleartext associations. This application sets a fitness for a substitution cipher key alphabet by the number of correct English words it produces. How might that change if the fitnesses were based on n-grams which included blank spaces, sub-words, word pairs and/or grammar?

Lock in the second and third words as 'we are.'
Lock 'H' to 'e'
Lock 'K' to 'w'
Lock 'P' to 'a'
Lock 'X' to 'r'

With these associations locked in, 100 runs of 500 generations will produce interesting results in a short time!

Evolutionary runs of 100,000 generations (70 min) yielded a max of $847,557.31 dollars. (9/23/2017)
Top score trading sequence.
Evolutionary runs of 20,000 generations (10 min) yielded a max of 510 thousand dollars.
Evolutionary runs of 10,000 generations (4 min) yielded a max of 337 thousand dollars.
Evolutionary runs of 5,000 generations (150 sec) yielded a max of 336 thousand dollars.
Evolutionary runs of 2,000 generations (60 sec) yielded a max of 89 thousand dollars.
Evolutionary runs of 1,000 generations (30 sec) yielded a max of 54 thousand dollars.

Our Evolutionary Concert Tour (ECT) application, stripped-down, can find the shortest path among 200 cities. In EvoImage 14, we modified it to reorder a sequence of 200 scrambled lines in a shredded image.

XE6 EvoImage 14
24 February 2015

EvoImage 14
29 February 2012
an Evolutionary Image Reconstructor
Adapted from ECT Version 13 stripped.

Open a scrambled picture (it must be a 200x200 .bmp).
The original image will appear at top-left. The line-to-line dis-similarity of the original image will appear at bottom-left. A scrambled line version of the original image will appear at top-right.

Set '100 Runs of ... generations' to 5000 and press 'Go.'
Every few seconds an evolved de-scrambled image will appear at bottom-right. If you wait a few minutes for 100 de-scrambled images to appear you can scroll through them using the TrackBar at bottom.

The point of this application is NOT to completely restore an image. After all, EvoImage may evolve an arrangement of lines that has greater line-to-line similarity than the original. The point of this application is to show that A SIMPLE FITNESS FUNCTION working with SIMPLE EVOLUTIONARY MECHANISM can do surprisingly well. Moreover, it visually replicates many of the artifacts that arise in biological evolution: Hill-climbing to a local peak on a fitness landscape and missing the global optimum as evidenced by highly fit sequences from a local perspective (see the eyes) but which are reversed with respect to one another at a global perspective.

200x200 bitmap (.bmp) images to unscramble:

Meteoritics set:

xxxxxxxxx

xxxxxxxxx

Current events set:

xxxxxxxxxxxx

xxxxxxxxxxxx

200x200 bitmap (.bmp) images to scramble and unscramble:

xxxxxxxxxxxx

Version 14 - 'Afghan Girl,' Thirteen best reconstructions. Top left image is original. Moving to the right is a line-to-line dissimilarity plot
and then the image with its lines scrambled (randomized). The thirteen images which follow are the 'best' results of 100 runs of 5000
generations of evolution. Note the similarities of these results to those of biological evolution:
Long sequences of lines are correctly grouped together, but as the sequences grow, they are more likely to be disrupted by recombination.

Evolutionary Sequence Optimizer (ESO): Version 13-S
(Stripped and extended to 100 cities in preparation for EvoImage)
13 October 2011
This version is a stripped down from Version 13 with the ability to take any number of cities (but with 100 set as its default). The second window and all the constraints, boundaries and bonuses have been removed. The visualizations of the evolutionary process have been retained for convenience. The visualization of the specific problem, which is assumed not to be the TSP, will need to be rebuilt. The variable names have been changed in order to facilitate adapting the application to other sequence-based evolutionary problems.

Some candidate applications include:

1. To Do: Cryptanalysis of a monoalphabetic cipher: Each individual solution will be a plaintext-to-ciphertext key. Each individual solution will use this key to attempt a decryption. The decryption will be evaluated on the basis of the number of common English language digraphs, trigraphs and quadragraphs it contains.
2. Done (see EVOIMAGE above): A deshredder. Reconstructing an image which has been sliced into rows and randomly reassembled. Each individual's row ordering will be evaluated on its similarity to adjacent rows. All the pixels in the row may be evaluated for their Red, Green and Blue similarities, or only one or two columns may be evaluated. It will be interesting to know how many columns need to be sampled to find a valid reconstruction. This procedure is directly relevant for archaeological seriation, biological cladistics and many other multivariable problems.
   

ect-3d-knot.txt

FAST: XE6 Evolutionary Concert Tour (ECT)
Version 20, 26 Sep 2015

For the background, two TImage objects are created:

ImageBackground, which is not visible, is never drawn upon and takes its Picture property from a 500 x 500 bitmap.

ImageToDrawOn, which is visible, its Canvas is drawn upon and to refresh itself takes its Picture property from ImageBackground.

Once ImageToDrawOn is completed, it is sent to the PaintBox in one statement.

Download the zipped file into a newly created folder. Unzip the .zip file into that folder. (By default the browser will want to add a deeper folder. Delete that default folder from the path.)
Open Embarcadero. Open the project file.
Use 'View/Units' to bring 'Unit1' and 'Unit2' into the IDE if necessary.
Use 'View/Forms' to bring the Forms into view if necessary.
You should now be able to modify the GUI and the code...

SLOW: Evolutionary Concert Tour (ECT)
Compact Version 15 - 25 February 2013

This version contains added constraints.
The target and radial plots are individually useful for evaluating the results of several different rules. .

Evolutionary Concert Tour - Matt Newcomb - March 2010
Bitmap added to represent terrains with different mobilities
A* ('a-star') maze-solving shortest-path algorithm added allowing meandering paths

• Source Code(not portable)
• Project Files Zipped(not portable)

Import terrain as a bitmap:

• Roads (with varying traffic congestion)

Import cities as a text file:

• Roads (with varying traffic congestion)

Evolutionary Concert Tour (ECT) - Compact Version 13 - 24 February 2011
This version is the same as Version 12 but with a 'left loops' constraint.

Evolutionary Concert Tour (ECT) - Compact Version 12 - 22 February 2011
This version is essentially the same as Version 11 Compact. Some functionality has been improved and constraints have been added for movement Eastward and for Equidistance (not changing the successive path lengths by more than 33).

An example of a variety of experiments to test these fitness functions using a configuration of cities that would be sensitive to the Equidistance constraint is here, followed by the test pattern for cities:

1. ect-equidistance.txt
   

Evolutionary Concert Tour (ECT) - Compact Version 11 - 17 February 2011
This version is essentially the same as Version 11. The Classroom #6 lecturer's station, display and classroom projector no longer accommodate the size of the previous version 11. In this version, much of the real estate has been moved to a second Window (Unit2 and Form2). The remaining primary Window has been shrunk-to-fit (hopefully). Let me know if there are any bugs...

Evolutionary Concert Tour (ECT) - Version 11 - 1 November 2010
Sequence penalties added:
Visit 24 satellites from 6 bases with 4 satellites between bases
Visit cities radiating outwards from center (get out of town)
Visit cities clockwise around center (twister)

Other improvements include:

• Three 30-city patterns are included below to test '4 Red 1 Green' satellite/base rule.
• TrackBar facilitates examining close examination of each of 100 solutions.
• TrackBar selection is mapped onto histogram with a red ShapeDot object.
• Floating point numbers have been formatted to two decimal places.
• Variable, function and object names as well as captions and labels have been modified to more intuitively reflect the internal operation of the application.

Evolutionary Concert Tour (ECT)
Version 10a (Obstacle problem corrected.)
11 March 2010
Multiple City Types, File Save/Open Cities & Multiple Obstacles.
CITY TYPES:
This version creates 2 city types by default. The user may place cities with of up to 4 types by combining the Right and Left mouse clicks (how to do it) with the pressed and not pressed Shift key. Any distribution of cities may be reassigned 4 types randomly, or geographically by quadrant or by vertical bar. Type colors will NOT show if 'ANY' is chosen, since with that option, city type is irrelevant. Otherwise, fitness preference can be assigned for visiting different or same types of cities in sequence, or visiting cities in rank descending order (e.g. 3, 2, 1, 0). Users can add new rule-based fitness functions easily in the calcPathFitness() function beginning at about line 459.
OBSTACLES:
Up to 4 obstacles may be added to define geographically-based fitness allocations. The cities, city types, obstacle locations and obstacle penalties can be saved and opened. This will be convenient for anyone wishing to experiment with different combinations of rule-based and geographically-based fitnesses for given distributions of cities and city types.
INTERFACE:
Controls have been regrouped in a more logical fashion. Some variable, function and object names have been changed to better represent their functions.

WHAT'S WRONG WITH AN ENGINEERING HEURISTIC?

In Engineering, one heuristic for solving large complex problems is to decompose them into smaller simpler parts. One solves those parts separately. Finally one reassembles them back into the larger whole. This does not always work well and we can illustrate that by the following experiments:
Decomposing the ECT cities into '4 Types Quads' by quadrants and selecting 'Same' works reasonably well except it fails dramatically with 'C-shaped' city distributions.
Decomposing the ECT cities into '4 Types Bars' by vertical bars and selecting 'Same' does not work well.
In large complex systems whose interactions and interrelationships are largely unknown in advance; problem solving by 'decomposition' is not a reliable approach.

Evolutionary Concert Tour (ECT)
Version I - 9
4 March 2010
Multiple City Types, Multiple Obstacles, Separation of Fitness from Distance
In Engineering, one heuristic for solving large complex problems is to decompose them into smaller simpler parts, solve those parts separately, and recompose them back into the whole. This works well if the structural interrelationships of the large complex problem are known so that it can be separated into meaningfully different modules. It does not work well if the process of decomposition 'cuts into' the natural clusters of interrelationships of the original problem. Decomposing the ECT into quadrants works reasonably well for most distributions of cities. It fails with 'C-shaped' distributions. Decomposing the ECT into parallel slices does not work well. With the ECT, the 'natural' solution is a loop, so a decomposition into quadrants is a better fit. Multiple city types (2 or 4) have been added. Four bonus/penalty border/boundaries have been added. The concepts of 'fitness' and 'distance' have been logically separated and 'distance' is shown as white dots on the 'Histogram of Current Population Fitness.' Save and Open do NOT save penalty boxes.

Evolutionary Concert Tour (ECT)
Version H - 8
1 October 2009
Variable Parent/Child ratios
Variable MultiRun Generations
The population histogram has also been modified to identify each of the parents (wide bars with a different color for each parent) and each of the children (narrow bars showing the parent's color) for each generation. Note that in early generations, the children are generally more fit than the parents, compared to the later generations in which the parents are generally more fit than the children. As evolution progresses we would probably like to decrease the variability.

Table of results of 500 generations for runs of each Parent/Child ratio.

Evolutionary Concert Tour (ECT) with Political Enhancement
Version G - 29 October 2008 - (derived directly from version F)
Suppose the dots represent households and not cities. Also suppose that you are not only interested in minimizing the physical distance of canvassing all the households, but you have a preference reasons for visiting:
a) Only Republicans first and then Democrats (so you don't have to change your interviewing style), or
b) Alternating between Republicans and Democrats (to stay attuned to contrasts).
The application will now allow you to place a varying 'penalty' on deviating from your preference.
The penalty is added to the length of your circuit.
If you have no preference, you may select 'Any' which is chosen by default.

To do:
The standardized configurations below confirm that the different fitness criteria do work. Create some standard configurations to run in 'Any,' 'Same' and 'Different' mode, with and without physical border penalties. Play with the penalty value (it is probably set too high). Vary it to discover the 'tipping points' with all other parameters set the same. The code may now be easily modified to expand the 'city' types to three or more and to develop various complex valuation schemes for different circuits through the cities.

Thanks to Kathleen Schaefer and 'IT' Itohan Aghayere for their efforts in thinking their ways down this road to enhancing the functionality of the application.

Some standardized configurations are below:

• Intersecting loops of different affiliations: political-a.txt
• Intersecting loops of alternating affiliations: political-b.txt

Evolutionary Concert Tour (ECT) May 2007 - Version F
For from 4 to 30 Cities w/Borders
Any number of cities may be introduced, from 4 to 30. The MouseWheel has been introduced and the sensitivity of the '100 Runs' plots to MouseMove has been changed. Many improvements could still be made. The file open and file save functions expect the first data element to be the number of cities, so the old configurations will need to be edited to work with the new version of the program.

Some standardized configurations are below:

Evolutionary Concert Tour (ECT) 2007 - February
w/ Bonus or Penalties for Crossing Borders
You may introduce a rectangular area. Crossing its border will either confer a bonus or extract a penalty as a multiple of the length of the path segment that crosses it. Click on 'Border' and then MouseDown, Drag and MouseUp to create the bounded region. Use the TrackBar to set the bonus or penalty multiplier. All possible path segment distances (including penalties) are recalculated every time you selecting the 30 cities or change the bonus/penalty.

Some standardized configurations are below:

2006 - December
Evolutionary Concert Tour (ECT)
with Penalties for Crossing Borders
You may introduce a rectangular area as an obstacle. Crossing its border will extract a toll (penalty) equal to a multiple of the length of the path segment that crosses it. Click on 'Border' and then MouseDown, Drag and MouseUp to create the bounded region. Use the TrackBar to set the penalty multiplier (2 or higher). All possible path segment distances (including penalties) are calculated on selecting the 30 cities or on changing the penalty.

2006 - November
Evolutionary Concert Tour (ECT)
File Save/Open and Obstacles
In addition to the functionality of the previous version, this version will open and save the user's configuration of 30 cities.

It will also introduce a vertical obstacle which extracts a penalty of up to 20 times the distance for the path legs which cross it. You may consider this to be a toll extracted for crossing a bridge over a river or a customs tariff charged for moving goods across a border.

Notice that the penalty is multiplied by the length of the leg that crosses the line. Consequently, not only must evolution minimize the number of crossings, but it must minimize the lengths of the legs which cross as well.

2006 - Evolutionary Concert Tour (ECT)
Multiple Runs
In addition to the functionality of the previous version, this version will collect and display statistics on 100 runs of 1000 generations each (10,000,000 individuals). In addition, the path and distance of the best solution of each run is stored . As each run is completed, a black dot is displayed showing its best solution distance along the X axis. Consecutive runs are plotted below the previous ones along the Y axis. The maximum and minimum distances are calculated and the scatter plot in black is then rescaled and replotted as a histogram in black, the number of occurrences at each distance shown against the blue scale as the absolute number out of one hundred, or percentage. By mousing over the histogram, you can recall the pathway that gave that distance as well as the rank of that distance. From the difference in successive ranks you can calculate the height of each histogram spike.

This version will also select for the longest (as opposed to the shortest) path, which we might call 'The New York Taxi Driver's Problem.'

2006 - Evolutionary Concert Tour (ECT)
Showing the Best of Each Generation.
A new plot has been added which shows the course of evolution - the best individual's path distance for each generation. Additionally, the path of the best individual's distance is shown for each generation, which provides a visual indication of the 'progress' of evolution. The application has been changes so that it only shows information relevant to Fogel's cities when Fogel's cities are chosen.

xx
xx

2005 - Evolutionary Concert Tour (ECT)
The old 'Traveling Salesman's Problem' (TSP) recast in contemporary terms. How can one travel to all cities and return home covering the least distance? There are1,409,286,144 (30!)possible paths, or loops, through the 30 cities. However, many are the same length: For every forward loop there is a reversed loop and for every loop there are 30 possible starting points. Consequently we must divide the total by 2x30 to get the number of different paths, which is 23,488,102. An exhaustive search of all possible paths is impractical, but we can use Darwinian/Wallacian evolution through natural selection to find the optimal path.

The user may select David Fogel's published arrangement of 30 cities, a random one or select her own. A first generation of one hundred agents are created, each representing one random path. In subsequent generations, the best half of the population serves as parents, replacing the worst half with variant offspring byasexual reproduction. The vector operations of shuffle, rotate and reverse are used to resequence the paths. For each agent, the best path is shown as well as the lengths of the best, average and worst paths. These visualizations may be switched off to speed the simulation. Thumbnails show some near optimal solutions to a spiral arrangement of cities.

Daisuke Imai's EVOcuation with Obstacles
During emergencies under panic conditions, spaces can be evacuated with maximal speed and minimal injury by introducing obstacles near the exit. This highly original and sophisticated project simulates evacuation with no, one and three obstacles, calculating both speed and injury. Additionally, both genetic algorithms and random search may be used to optimize obstacle configurations. Individual agent perceptions and behaviors are complex.

Executable
Source Code Unit1
Source Code Unit2

Saeid Atoofi's Language Evolution
(on the assimilation of language elements)

2006 Evolutionary Iterated Prisoners Dilemma
An evolutionary enhanced version of the classic game.
Evolution is achieved by randomly changing the strategy of the worst player every 100 games.
Devolution is achieved by randomly changing the strategy of the best player every 100 games.
Evolution changes the screen to a pale green.
Devolution changes the screen to a pale pink.
A high not is played when an evolutionary change is made. A lower note is played when a devolutionary change is made.

Garret's Evolutionary Iterated Prisoners Dilemma
An evolutionary enhanced version of the classic game.

2005 - Adam Skory's Evolutune
Evolutune is a program to evolve little musical loops. Each loop consists of four measures of beeps and boops. These loops are created based on sets of parameters, or genotypes, that define their characteristics. By selecting those loops that sound best, and reproducing them with slight mutations in their genotypes, a user can direct the evolution of the loops into being 'more fit' for their environment (i.e. your ear).

The green triangle button is PLAY.
The black square button is STOP.
The red RECORD button is not implemented.

Rearranging a 1-D ARRAY using a VECTOR
This program shows how to use a 1-D VECTOR and the mutation and sorting algorithms in the Standard Template Library in order to manipulate a 1-D ARRAY directly. This is the first step in manipulating a 2-D ARRAY.

Rearranging a 2-D ARRAY using a VECTOR
This program shows how to use a 1-D VECTOR and the mutation and sorting algorithms in the Standard Template Library in order to manipulate the indices to a 2-D ARRAY. The result is that the ARRAY can be subjected to the same mutations as the VECTOR.

Rearranging a TPoint ARRAY using a VECTOR
This program shows how to use a VECTOR of TPoint ARRAY structure and the mutation and sorting algorithms in the Standard Template Library in order to manipulate its contents. The VECTOR may than be copied back into the TPoint ARRAY.

Tim's A* Maze Solving Algorithm
Recompiled with Embarcadero, 25 February 2013.
The 'A-Star' search algorithm finds the shortest path between source and target cells (red) in a maze. The path is traced in cyan, if a path is possible at all.

Tim's A* Maze with Bitmap
Recompiled with Embarcadero, 25 February 2013.
Instead of creating random mazes, this version allows you to import a 61 by 61 cell bitmap image drawn in black-and-white. Two such images are included. Pictorial images may be reduced in PhotoShop for different effects.

Two bitmaps: