几何语义GP与梯度优化器的杂交研究

IF 1.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Gloria Pietropolli, Luca Manzoni, Alessia Paoletti, Mauro Castelli
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引用次数: 0

摘要

几何语义遗传规划(GSGP)是语义遗传规划的一种流行形式,其中交叉和突变的影响可以表示为语义空间上的几何运算。最近的一项研究表明,GSGP可以与标准的基于梯度的优化算法Adam杂交,后者通常用于训练人工神经网络。我们通过考虑更多基于梯度的优化器、对它们的参数进行更深入的研究、如何执行杂交以及更全面的基准问题集来扩展这项工作。通过对超参数的正确选择,这种杂交方法提高了GSGP的性能,使其能够以较少的适应度评价达到相同的适应度值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

On the hybridization of geometric semantic GP with gradient-based optimizers

On the hybridization of geometric semantic GP with gradient-based optimizers
Abstract Geometric semantic genetic programming (GSGP) is a popular form of GP where the effect of crossover and mutation can be expressed as geometric operations on a semantic space. A recent study showed that GSGP can be hybridized with a standard gradient-based optimized, Adam, commonly used in training artificial neural networks.We expand upon that work by considering more gradient-based optimizers, a deeper investigation of their parameters, how the hybridization is performed, and a more comprehensive set of benchmark problems. With the correct choice of hyperparameters, this hybridization improves the performances of GSGP and allows it to reach the same fitness values with fewer fitness evaluations.
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来源期刊
Genetic Programming and Evolvable Machines
Genetic Programming and Evolvable Machines 工程技术-计算机:理论方法
CiteScore
5.90
自引率
3.80%
发文量
19
审稿时长
6 months
期刊介绍: A unique source reporting on methods for artificial evolution of programs and machines... Reports innovative and significant progress in automatic evolution of software and hardware. Features both theoretical and application papers. Covers hardware implementations, artificial life, molecular computing and emergent computation techniques. Examines such related topics as evolutionary algorithms with variable-size genomes, alternate methods of program induction, approaches to engineering systems development based on embryology, morphogenesis or other techniques inspired by adaptive natural systems.
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