The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming

• URL: http://arxiv.org/abs/2205.02598v1
• Date: Thu, 5 May 2022 12:26:25 GMT
• Title: The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming
• Authors: Mauro Castelli, Luca Manzoni, Luca Mariot, Giuliamaria Menara, Gloria Pietropolli
• Abstract summary: Geometric Semantic Genetic Programming (GSGP) has shown to be successfully applicable to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all the evolutionary history, i.e., all populations from the first one. We exploit this stored information to define a multi-generational selection scheme that is able to use individuals from older populations.
• Score: 8.0322025529523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Among the evolutionary methods, one that is quite prominent is Genetic Programming, and, in recent years, a variant called Geometric Semantic Genetic Programming (GSGP) has shown to be successfully applicable to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all the evolutionary history, i.e., all populations from the first one. We exploit this stored information to define a multi-generational selection scheme that is able to use individuals from older populations. We show that a limited ability to use "old" generations is actually useful for the search process, thus showing a zero-cost way of improving the performances of GSGP.

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