LEGO Robots and Evolution
Contributors:  Geoff Bingham: Author
Teresa Pegors: Author
Linda Smith: Author



The world contains an incredible variety of living organisms, from massive, slow-moving blue whales in the Pacific, to small spiders that scuttle away at the slightest disturbance, or eagles, which soar in spectacular majesty above the earth. Most students have been taught a bit about the theory of evolution, which proposes that the variety in species of animals comes about through a process of selection, reproduction, and mutation. Because this process takes place over such a great amount of time, students have a difficult time conceptualizing the ways in which evolution can find simple solutions to issues of survival.

In 1975, John Holland first devised genetic algorithms (GA’s) as a way in which the biological processes of evolution could be modeled computationally. In the last quarter century, the use of genetic algorithms has become a popular way of using many of the simple mechanisms of evolution to practically evolve solutions to problems.

This workshop allows the students to participate in the use of a genetic algorithm strategy to evolve the structure of LEGO robots for achieving a specific goal. The students are first introduced to the basics of how genes are involved in biological evolution and how genetic algorithms use these processes computationally. Groups are then formed in which members are responsible for the tasks associated with evolving their group’s robot. The robots’ performance in the environment is measured according to a pre-specified goal, and selection and reproduction take place manually between the groups.

Not only do students become more aware of the capabilities of evolution, but they get a different perspective on how problems can be solved, and gain experience working with LEGO© Mindstorms™.



This module was created by the Indiana University Cognitive Science Program and is also available on their website: IU Cognitive Science Software.


This module was supported by National Science Foundation Grant #0127561.