Evolving Robust Solutions for Stochastically Varying Problems

We demonstrate how evaluating candidate
solutions in a limited number of stochastically varying
conditions that vary over generations at a moderate rate is an
effective method for developing high quality robust solutions.
Indeed, agents evolved with this method for the ability to solve
an extended version of the double-pole balancing problem, in
which the initial state of the agents and the characteristics of
the environment in which the agents are situated vary, show
the ability to solve the problem in a wide variety of
environmental circumstances and for prolonged periods of
time without the need to readapt. The combinatorial explosion
of possible environmental conditions does not prevent the
evolution of robust solutions. Indeed, exposing evolving agents
to a limited number of different environmental conditions that
vary over generations is sufficient and leads to better results
with respect to control experiments in which the number of
experienced environmental conditions is greater. Interestingly
the exposure to environmental variations promotes the
evolution of convergent strategies in which the agents act so to
exhibit the required functionality and so to reduce the
complexity of the control problem.

Publication type: 
Contributo in volume
Author or Creator: 
Carvalho, Jonata Tyska
Milano, Nicola
Nolfi, Stefano
IEEE Computer Society Press, Loa Alamitos [CA], USA
Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1361–1368. Loa Alamitos [CA]: IEEE Computer Society Press, 2018
info:cnr-pdr/source/autori:Carvalho, Jonata Tyska; Milano, Nicola; Nolfi, Stefano/titolo:Evolving Robust Solutions for Stochastically Varying Problems/titolo_volume:Proceedings of the IEEE Congress on Evolutionary Computation (CEC)/curatori_volume:/editor
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