scholarly journals Penalty Functions for Genetic Programming Algorithms

Author(s):  
José L. Montaña ◽  
César L. Alonso ◽  
Cruz Enrique Borges ◽  
Javier de la Dehesa
MENDEL ◽  
2018 ◽  
Vol 24 (2) ◽  
Author(s):  
Tomas Brandejsky

This paper analyses the influence of experiment parameters onto the reliability of experiments with genetic programming algorithms. The paper is focused on the required number of experiments and especially on the influence of parallel execution which affect not only the order of thread execution but also behaviors of pseudo random number generators, which frequently do not respect recommendation of C++11 standard and are not implemented as thread safe. The observations and the effect of the suggested improvements are demonstrated on results of 720,000 experiments.


2002 ◽  
Vol 10 (1) ◽  
pp. 51-74 ◽  
Author(s):  
Peter Bruhn ◽  
Andreas Geyer-Schulz

In this paper, we introduce genetic programming over context-free languages with linear constraints for combinatorial optimization, apply this method to several variants of the multidimensional knapsack problem, and discuss its performance relative to Michalewicz's genetic algorithm with penalty functions. With respect to Michalewicz's approach, we demonstrate that genetic programming over context-free languages with linear constraints improves convergence. A final result is that genetic programming over context-free languages with linear constraints is ideally suited to modeling com-plementarities between items in a knapsack problem: The more complementarities in the problem, the stronger the performance in comparison to its competitors.


2019 ◽  
pp. 27-62
Author(s):  
Johnathan Melo Neto ◽  
Heder S. Bernardino ◽  
Helio J.C. Barbosa

2018 ◽  
Vol 15 (3) ◽  
pp. 635-654 ◽  
Author(s):  
Josefa Álvarez ◽  
Franciso Chávez ◽  
Pedro Castillo ◽  
Juan García ◽  
Francisco Rodriguez ◽  
...  

In recent years, the energy-awareness has become one of the most interesting areas in our environmentally conscious society. Algorithm designers have been part of this, particularly when dealing with networked devices and, mainly, when handheld ones are involved. Although studies in this area has increased, not many of them have focused on Evolutionary Algorithms. To the best of our knowledge, few attempts have been performed before for modeling their energy consumption considering different execution devices. In this work, we propose a fuzzy rulebased system to predict energy comsumption of a kind of Evolutionary Algorithm, Genetic Prohramming, given the device in wich it will be executed, its main parameters, and a measurement of the difficulty of the problem addressed. Experimental results performed show that the proposed model can predict energy consumption with very low error values.


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