Christian Jacob - Simulating Evolution with Mathematica

Mike's Notes

I have collected here some historical references to work by Christian Jacob, a Professor at the University of Calgary, about evolutionary algorithms. There are many Mathematica Notebooks available in Mathematica version 2.2. I hope to use the Wolfram-provided copy of WolframOne to open and try out these notebooks.

These references are for future planned Pipi developments.

Resources

• https://profiles.ucalgary.ca/christian-jacob
• https://www.swarm-design.org/
• https://cspages.ucalgary.ca/~cjacob/Evolvica/Downloading.html
• https://cspages.ucalgary.ca/~cjacob/Evolvica/
• https://cspages.ucalgary.ca/~cjacob/Evolvica/Evolvica-Intro.html
• https://www.witpress.com/elibrary/wit-transactions-on-engineering-sciences/15/7919
• https://library.wolfram.com/infocenter/Articles/2616/
• https://library.wolfram.com/infocenter/Courseware/282/
• https://library.wolfram.com/infocenter/MathSource/569/
• https://www.semanticscholar.org/paper/Evolvica-%E2%80%93-A-Framework-for-Evolutionary-Computation-Rummler-Strufe/756a6382817b12d556c901de6b5138ae07ca8a6d
• https://www.computingreviews.com/review/review_review.cfm?review_id=126241
• https://www.computingreviews.com/browse/browse_topics.cfm?CFID=114039441&CFTOKEN=59205861

References

• Simulating Evolution with Mathematica (1997) by Christian Jacob
• Illustrating evolutionary computation with Mathematica by Christian Jacob. Morgan Kaufmann Publishers Inc., San Francisco, CA, 2001. 578 pp. Type: Book (9781558606371)
• Holland, J.H. Adaptation in Natural and Artificial Systems : An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge, MA, 1992.

Repository

• Home > Ajabbi Research > Library > Authors > Christian Jacob
• Home > Ajabbi Research > Library > Evolutionary Algorithm
• Home > Ajabbi Research > Library > Membrane Computing

Last Updated

23/04/2025

Simulating Evolution with Mathematica

By: Christian Jacob

Google Scholar: 1997

Evolutionary mechanisms as observed in nature are successfully used in evolutionary algorithms (EA) in order to solve complex optimisation tasks or to mimic natural evolution processes. We present a collection of evolutionary algorithms which we have implemented in Mathematica, together with some visualisation examples and applications. The three major EA classes are discussed: Evolution Strategies (ES), Genetic Algorithms (GA), and Genetic Programming (GP). Interactive evolution is demonstrated by the breeding of biomorphs, recursively branched line drawings. Multi-modal ES- and GA- experiments are demonstrated for a parameter optimisation task. The evolution of robot control programs shows a simple GP application. The article concludes with a more sophisticated GP example: breeding developmental programs for artificial plant-like structures encoded based on Lindenmayer systems.