Self-adaptive exploration in evolutionary search
<|reference_start|>Self-adaptive exploration in evolutionary search: We address a primary question of computational as well as biological research on evolution: How can an exploration strategy adapt in such a way as to exploit the information gained about the problem at hand? We first introduce an integrated formalism of evolutionary search which provides a unified view on different specific approaches. On this basis we discuss the implications of indirect modeling (via a ``genotype-phenotype mapping'') on the exploration strategy. Notions such as modularity, pleiotropy and functional phenotypic complex are discussed as implications. Then, rigorously reflecting the notion of self-adaptability, we introduce a new definition that captures self-adaptability of exploration: different genotypes that map to the same phenotype may represent (also topologically) different exploration strategies; self-adaptability requires a variation of exploration strategies along such a ``neutral space''. By this definition, the concept of neutrality becomes a central concern of this paper. Finally, we present examples of these concepts: For a specific grammar-type encoding, we observe a large variability of exploration strategies for a fixed phenotype, and a self-adaptive drift towards short representations with highly structured exploration strategy that matches the ``problem's structure''.<|reference_end|>
@article{toussaint2001self-adaptive,
    title={Self-adaptive exploration in evolutionary search},
    author={Marc Toussaint},
    journal={arXiv preprint arXiv:physics/0102009},
    year={2001},
    archivePrefix={arXiv},
    eprint={physics/0102009},
    primaryClass={physics.bio-ph cs.NE nlin.AO q-bio}
}