A Technique for Evaluation of Interactive Evolutionary Systems

Building more open-ended evolutionary systems can simultaneously advance our understanding of biology, artificial life, and evolutionary computation. In order to do so, however, we need a way to determine when we are moving closer to this goal. We propose a set of metrics that allow us to measure a system's ability to produce commonly-agreed-upon hallmarks of open-ended evolution: change potential, novelty potential, complexity potential, and ecological potential. Our goal is to make these metrics easy to incorporate into a system, and comparable across systems so that we can make coherent progress as a field. To this end, we provide detailed algorithms (including C++ implementations) for these metrics that should be easy to incorporate into existing artificial life systems. Furthermore, we expect this toolbox to continue to grow as researchers implement these metrics in new languages and as the community reaches consensus about additional hallmarks of open-ended evolution. For example, we would welcome a measurement of a system's potential to produce major transitions in individuality. To confirm that our metrics accurately measure the hallmarks we are interested in, we test them on two very different experimental systems: NK Landscapes and the Avida Digital Evolution Platform. We find that our observed results are consistent with our prior knowledge about these systems, suggesting that our proposed metrics are effective and should generalize to other systems.

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GENETIC REPRESENTATION AND GENETIC NEUTRALITY IN GENE EXPRESSION PROGRAMMING

Advances in Complex Systems ◽

10.1142/s0219525902000626 ◽

2002 ◽

Vol 05 (04) ◽

pp. 389-408 ◽

Cited By ~ 33

Author(s):

CÂNDIDA FERREIRA

Keyword(s):

Gene Expression ◽

Molecular Evolution ◽

Gene Expression Programming ◽

Neutral Theory ◽

Artificial System ◽

Evolutionary Systems ◽

Theory Of Evolution ◽

Coding Regions ◽

Neutral Mutations

The neutral theory of molecular evolution states that the accumulation of neutral mutations in the genome is fundamental for evolution to occur. The genetic representation of gene expression programming, an artificial genotype/phenotype system, not only allows the existence of non-coding regions in the genome where neutral mutations can accumulate but also allows the controlled manipulation of both the number and the extent of these non-coding regions. Therefore, gene expression programming is an ideal artificial system where the neutral theory of evolution can be tested in order to gain some insights into the workings of artificial evolutionary systems. The results presented in this work show beyond any doubt that the existence of neutral regions in the genome is fundamental for evolution to occur efficiently.

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