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Evolving a predator–prey ecosystem of mathematical expressions with grammatical evolution
Author(s) -
Alfonseca Manuel,
Soler Gil Francisco José
Publication year - 2014
Publication title -
complexity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 61
eISSN - 1099-0526
pISSN - 1076-2787
DOI - 10.1002/cplx.21507
Subject(s) - predation , predator , range (aeronautics) , selection (genetic algorithm) , constant (computer programming) , ecological niche , ecology , natural selection , ecosystem , niche , rate of evolution , mathematics , biology , computer science , artificial intelligence , biochemistry , materials science , phylogenetics , habitat , gene , composite material , programming language
This article describes the use of grammatical evolution to obtain a predator–prey ecosystem of artificial beings associated with mathematical functions, whose fitness is also defined mathematically. The system supports the simultaneous evolution of several ecological niches and through the use of standard measurements, makes it possible to explore the influence of the number of niches and the values of several parameters on “biological” diversity and similar functions. Sensitivity analysis tests have been made to find the effect of assigning different constant values to the genetic parameters that rule the evolution of the system and the predator–prey interaction or of replacing them by functions of time. One of the parameters (predator efficiency) was found to have a critical range, outside which the ecologies are unstable; two others (genetic shortening rate and predator–prey fitness comparison logistic amplitude) are critical just at one side of the range), the others are not critical. The system seems quite robust, even when one or more parameters are made variable during a single experiment, without leaving their critical ranges. Our results also suggest that some of the features of biological evolution depend more on the genetic substrate and natural selection than on the actual phenotypic expression of that substrate. © 2014 Wiley Periodicals, Inc. Complexity 20: 66–83, 2015

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