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Mutation and selection strategies for incremental evolution

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If you have a question about this talk, please contact Leandro Minku.

Host: Peter Tino

Artificial evolutionary systems often exhibit very low diversity and bounded complexity. I will give an overview of a number of projects, carried out with research partners and students, that include addressing these shortcomings within their aims: research into critical mutation rates for the maintenance of allelic diversity; mutation rate control for extinction-avoiding novel adaptation; the role of transcription errors in discovering behaviours inaccessible to incremental genetic evolution alone; and selection complexification strategies for incremental evolution. I will then look forward and discuss three approaches to the evolution of intelligent agents, more specifically to tackling the requirement to find an evolutionary path from a random or primordial soup to a sufficient behaviour: scaffolding selection with increasingly complex tasks, searching for novel behaviours, and seeding evolution by artificial selection with the results of (long-term or open-ended) evolution by natural selection. Examples of evolved agents will be shown in simple two-and three-dimensional environments.

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This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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