Abstract
Evolutionary search is a global search method based on natural selection. In engineering curriculum, these techniques are taught in courses like Evolutionary Computation, Engineering Optimization, etc. Genetic algorithm (GA) is popular among these algorithms. Genetic programming (GP), developed by John Koza, is a powerful extension of GA where a chromosome/computer program (CP) is coded as a rooted point-labeled tree with ordered branches. The search space is the space of all possible CPs (trees) consisting of functions and terminals appropriate to the problem domain. GP uses, like GA, crossover and mutation for evolution. Due to tree-structured coding of individuals, the initial population generation, genetic operators' use, and tree decoding for fitness evaluations demand careful computer programming. This article describes the programming steps of GP implementation (using C++ language) for students' easy understanding with pseudocodes for each step. Two application examples are also illustrated. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 434–448, 2010; View this article online at wileyonlinelibrary.com; DOI 10.1002/cae.20165
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