Title :
Nonlocal evolutionary adaptation in gridplants
Author_Institution :
Comput. Sci., Iowa State Univ., Ames, IA, USA
Abstract :
A simulated model of plant growth and evolution was studied. Plants start out as seeds on a 2D grid. Plant genomes are modeled as instructions telling a plant where to grow and where to place seeds. Energy is gained by occupying grid space in analogy to collection of light by leaf surface area. At the end of a generation, cells currently occupied by plants are cleared and the seeds dropped by all the plants sprout to become the new plants. Each seed produced has a probability of mutation to the genome it contains. The simulated plants evolve to play a game of competitive exclusion, in which grid space is a limited resource. This work tested the hypothesis that the evolved plants would display nonlocal adaptation, i.e. that the plants would not only adapt to their local environment, but would acquire general skill that would enable them to grow competitively against plants that were never a part of their environment. Statistical tests show that populations of plants that have evolved for a larger number of generations are able to occupy more grid space when played against populations of plants evolved for a shorter time. This occurs even if the two competing populations come from entirely different lineages. This improvement in competitive ability continues over the course of the evolution performed in this study, without appearing to reach an equilibrium after which further evolution fails to improve the plants. This suggests that the plants are continually discovering generally useful strategies, rather than adapting only to their local environment.
Keywords :
adaptive systems; evolution (biological); evolutionary computation; living systems; 2D grid; competitive exclusion; evolution simulated model; grid space; gridplants; nonlocal evolutionary adaptation; plant genomes; plant growth simulated model; statistical tests; Bioinformatics; Biological system modeling; Computational biology; Computational modeling; Computer science; Computer simulation; Evolution (biology); Genomics; Organisms; Testing;
Conference_Titel :
Evolutionary Computation, 2004. CEC2004. Congress on
Print_ISBN :
0-7803-8515-2
DOI :
10.1109/CEC.2004.1331087