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On Synergistic Interactions Between Evolution, Development and Layered Learning
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AI
DOI：10.1109/TEVC.2011.2150752.png)
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Abstract
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We investigate interactions between evolution, development and lifelong layered learning in a combination we call evolutionary developmental evaluation (EDE), using a specific implementation, developmental tree-adjoining grammar guided genetic programming (GP). The approach is consistent with the process of biological evolution and development in higher animals and plants, and is justifiable from the perspective of learning theory. In experiments, the combination is synergistic, outperforming algorithms using only some of these mechanisms. It is able to solve GP problems that lie well beyond the scaling capabilities of standard GP. The solutions it finds are simple, succinct, and highly structured. We conclude this paper with a number of proposals for further extension of EDE systems.
Keywords:
Developmental
evaluation
genetic programming
incremental evolution
layered learning
modularity
regularity
structural
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12
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1.9K
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