SGP-DT: Semantic Genetic Programming Based on Dynamic Targets

• URL: http://arxiv.org/abs/2001.11535v1
• Date: Thu, 30 Jan 2020 19:33:58 GMT
• Title: SGP-DT: Semantic Genetic Programming Based on Dynamic Targets
• Authors: Stefano Ruberto and Valerio Terragni and Jason H. Moore
• Abstract summary: This paper presents a new Semantic GP approach based on Dynamic Target (SGP-DT) The evolution in each run is guided by a new (dynamic) target based on the residual errors. SGP-DT achieves small RMSE values, on average 23.19% smaller than the one of epsilon-lexicase.
• Score: 6.841231589814175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic GP is a promising approach that introduces semantic awareness during genetic evolution. This paper presents a new Semantic GP approach based on Dynamic Target (SGP-DT) that divides the search problem into multiple GP runs. The evolution in each run is guided by a new (dynamic) target based on the residual errors. To obtain the final solution, SGP-DT combines the solutions of each run using linear scaling. SGP-DT presents a new methodology to produce the offspring that does not rely on the classic crossover. The synergy between such a methodology and linear scaling yields to final solutions with low approximation error and computational cost. We evaluate SGP-DT on eight well-known data sets and compare with {\epsilon}-lexicase, a state-of-the-art evolutionary technique. SGP-DT achieves small RMSE values, on average 23.19% smaller than the one of {\epsilon}-lexicase.

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