A 'Hands on' Strategy for Teaching Genetic Algorithms to Undergraduates

Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are ‘intractable’ using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary literature, such as population, chromosome, crossover, cloning, mutation, genes and generations. For beginners studying genetic algorithms, there is quite an overhead in gaining comfort with these terms and an understanding of their parallel meanings in the unfamiliar computing milieu of an evolutionary algorithm.