Teaching Creativity: The Case for/Against Genetic Algorithms as a Model of Human Creativity

In this article we discuss how the creative processes can be modelled by genetic algorithms (GAs). Three fundamental aspects of human creativity need to be considered: 1) the emergence of a novel element; 2) the use of that element to achieve a goal; 3) the presence of obstacles imposed by the problem space. GAs have been proposed as a model of these aspects of human creativity. A GA, despite following procedures that are not intelligent is able to find solutions that can be recognised as intelligent. Examining GAs it is possible to prescribe principles that can be useful for human creativity. These principles include the memorisation, storage and retrieval of information as well as the combination and recombination of simple elements within a mechanical procedure of trial and error that formulate the foundation of a cumulative process. GAs can have an important role in the educational process. Education must teach not only rules and schemas as well as the capacity to focus on what information is relevant, but also must teach one to be open to new information and be elastic with it such that that which doesn’t immediately seem useful can remain for some time for “trial and error” experimentation that can produce results that are typical of human creativity. Combing these two fundamental aspects of human creativity (rules and elasticity) configures a model of education that can develop a mind that is both rigorous as well as creative.