## Archive for the ‘source code’ Category

## Simple artificial life program source code

I am finishing up my reading of ‘The Magic Machine: A Handbook of Computer Sorcery‘ and there were only two programs left to write. I thought I’d wipe the first one out in a day. Heh, it took three.

In a chapter of the book the author discusses early attempts at genetically evolving artificial life. He gives a rough algorithm and states he had all kinds of critters running around in just a few hundred generations. I loosely wrote a program based on his algorithm. 150,000+ cycles, and 36 hours on my computer later, no evolution. I don’t know how he did it? I couldn’t get the algorithm in the book to produce any interesting results.

I did today get a program that has bugs that learn to stay on and follow food lines drawn in the window. It takes about 1500 days ( cycles ) for them to achieve this universally. The source code is linked to below.

Here’s what I learned in my attempts at a very simple genetic program.

If you place food randomly there is nothing to learn. You just end up with a population of stupid bugs. Adjusting the food nutrient content worked better than adjusting the amount of food for controlling population levels and for evolution. Creating more food to meet the population just created lots of stupid bugs. ( I wonder if there is a real life lesson in that? )

If you adjust the bugs DNA when they find food, not just their energy levels they learn much faster.

I hope to do some more complex and interesting evolution programs soon.

Source Code:

Bugs.java

See also

Evolutionary AI for more information and several useful links and papers to get you started.

SantaFe Ants

## Source code to simulate people at a party

I’ve covered a fair bit of simulations of people in news stories and recently read a chapter on people simulations in ‘ The Magic Machine: A Handbook of Computer Sorcery‘ and thought it was time to try a simple simulation.

This source code simulates 3 groups at a party. One group has a personal comfort zone of 1 foot, one group a zone of two feet and one a zone of three feet. Each group is a different color. The people start in random locations. As the party progresses and people move about they try to keep other people at their personal comfort zone. Each person moves to the square nearest himself that maximizes the comfort zone for himself. If that is not possible the person moves to a random spot ( mingles ).

If you deduct happiness from a person when they are crowded or lonely they move more than if no happiness points are deducted. They mingle more, deducting no points the crowd is more likely to form groups.

The code is heavily commented and should be easy to follow, source code is in Java.

## Cellular Automata

In Wolfram’s book “A New Kind of Science” he studies cellular automata. What is also interesting is the approach he is taking. Rather than take something we know and try to figure out the rules, he tries different rules to see what they will create. While his book was badly received when it came out, other’s have found great success using this approach recently.

Wolfram was hardly the first to use cellular automata to mimick real life. Martin Gerhardt and Heike Schuster created ‘HodgePodge’ a cellular automata to mimic a chemical reaction. They explained it as using a discrete form of a differential equation. Since we have described most natural phenomena with differential equations that may explain why we have not done more with cellular automata. We just haven’t needed it. We may find use for it in things we can not yet easily describe with differential equations.

For example, cellular automata has found success in artificial intelligence in solving echohydraulics modeling problems. Cellular automata is also being used in pattern recognition, image processing, fluid mechanics and bioinformatics.

Cellular automata is simple rules repeated over and over. While one would expect simple patterns to emerge and repetitive patterns to emerge, non-repetitive and complex patterns also emerge in some cases. The two examples shown use only two colors and simple rules. Three colors leads to many more complex designs.

Nested: If either neighbor but not both neighbors in previous row are black-> color this cell black else color it white.

Irregular: If self and right neighbor in previous row are white then self is same color as left neighbor in previous row. If self and right neighbor are not both white then self is opposite color of left neighbor in previous row.

Source code:

Java automata examples

More information:

A New Kind of Science, Talk at US by Stephen Wolfram

CelLab, Cellular Automata Laboratory

A New Kind of Science ( book is available online and free)

Some of the recent controversy about Wolfram’s book

Fractal Geometry

Cellular Automata Links

An Introduction to Lindenman Systems ( related subject )

The Primordial Soup Kitchen

One dimensional cellular automata Java applet

A weakly universal cellular automaton in the hyperbolic 3D space with three states

## Biomorphs and artificial intelligence

I hadn’t heard of Biomorphs until I started wandering through The Magic Machine: A Handbook of Computer Sorcery. I’m brushing up on math and coding now so I can do some new projects this year and this book is a fun way to do it.

[ z = z^3 + c]

[ z = sin(z) + z^2 + c ]

Biomorphs were discovered by Clifford Pickover at the IBM Research Center. Biomorphs are like Mandelbrot functions in that you iterate a simple function over the complex plane. The algorithm is in the 2 example Java files below. Several more besides these two are known to reside between -20 and 20. { z^z + z^6 + c; z^z + z^6 + c; sin(z) + e^z +c; z^5 + c; z^z + z^5 + c; . . . } You should be able to figure out how to create them by altering the two examples in the download file.

Some people breed biomorphs and let them evolve as an artificial life form.

Source Code

biomorphs.tar.gz ( 2 Java source files for the biomorphs above )

More information:

Fractal Geometry

Dr. Clifford Pickover home page

Mad Teddy’s Fractals #2 Biomorphs

## Fractals and artificial intelligence

Fractals are a fascinating toy, one can easily spend an afternoon lost in Mandelbrot or Julia sets. Mathematicians were aware of fractals as early as the 1700s but it wasn’t until we had computers to do the calculations that we really discovered fractals.

Benoit B. Mandelbrot doing research at IBM was revisiting Gaston Julia’s work with fractals (1917) when he discovered the Mandelbrot set. Fractals are simple equations that are recursively computed. These simple equations create complex shapes.

The Mandelbrot function is z = z^2 + c. z and c are complex numbers, z is set to zero, c is the position on the x ( x, yi ) plane. You recursively compute this function to obtain the Mandelbrot fractal. Black is for the numbers that do not escape to infinity, the other colors represent how many loops it takes to escape.

Fractals have found some use in artificial intelligence. In the world of computer games, fractals create plant life, clouds, mountains and other scenery that would not be possible in such detail. Parkinson’s patients are diagnosed by their gait. In 2004 a sensor was developed that measures the patient’s gait, and analyzes the gait using fractals. 2002 fractals were put to use to help predict natural disasters and better model hurricanes. More recently fractal patterns have been found in solar wind. It is hoped this information will allow us to better predict solar storms.

Fractals have been found in Jackson Pollacks paintings and are being used to try to identify real paintings from fakes. They are also being used in image compression. A more fun way to play with fractals is to use them to predict the stock and commodity markets.

Fractals ( Mandelbrot and Julia in Java – source code )

More information:

Fractal Geometry

Fractals

Math on Display, Science News Online

Genius: Benoit Mandelbrot

3D Mandelbrot images

Papers:

The Fractal Geometry of Nature, Mandelbrot ( pdf/ps )