Contrôle de la croissance de la taille des individus en programmation génétique
|Advisor:||Gagné, Christian; Parizeau, Marc|
|Abstract:||Genetic programming is a hyperheuristic optimization approach that has been applied to a wide range of problems involving symbolic representations or complex data structures. However, the method can be severely hindered by the increased computational resources required and premature convergence caused by uncontrolled code growth. We introduce HARM-GP, a novel operator equalization approach that adaptively shapes the genotype size distribution of individuals in order to effectively control code growth. Its probabilistic nature minimizes the overhead on the evolutionary process while its generic formulation allows this approach to remain independent of the problem and genetic operators used. Comparative results are provided over twelve problems with different dynamics, and over nine other algorithms taken from the literature. They show that HARM-GP is excellent at controlling code growth while maintaining good overall performances. Results also demonstrate the effectiveness of HARM-GP at limiting overtraining and overfitting in real-world supervised learning problems.|
|Document Type:||Mémoire de maîtrise|
|Open Access Date:||20 April 2018|
|Collection:||Thèses et mémoires|
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