Akarsh Kumar
Effective Mutation Rate Adaptation through Group Elite Selection
Abstract:
Evolutionary algorithms are sensitive to the mutation rate (MR);
no single value of this parameter works well across domains.
Selfadaptive MR approaches have been proposed
but they tend to be brittle; for example,
they sometimes decay the MR to zero, thus halting evolution.
To make selfadaptive MR robust, this paper introduces the
Group Elite Selection of Mutation Rates (GESMR) algorithm.
GESMR coevolves a population of solutions and a population
of MRs, such that each MR is assigned to a group of solutions.
The resulting best mutational change in the group, instead
of average mutational change, is used for MR selection during evolution,
thus avoiding the vanishing MR problem.
With the same number of function evaluations
and with almost no overhead, GESMR converges faster
and to better solutions than previous approaches on a wide range of
continuous test optimization problems. GESMR also scales well to highdimensional
neuroevolution for supervised imageclassification tasks and for
reinforcement learning control tasks.
Analysis of the distribution of function changes during mutation
explains why selfadaptation is prone to premature convergence
and how GESMR overcomes this issue.
Empirically, GESMR produces MRs that are optimal in the longterm,
as demonstrated through a comprehensive lookahead grid search.
GESMR and the analysis have theoretical and practical
implications for the fields of artificial life and evolutionary computation.
Physically Plausible Pose Refinement using Fully Differentiable Forces
Abstract:
All handobject interaction is controlled by forces that the two bodies exert on each other,
but little work has been done in modeling these underlying forces when doing pose and contact
estimation from RGB/RGBD data.
Given the pose of the hand and object from any pose estimation system, we propose an endtoend
differentiable model that refines pose estimates by learning the forces experienced by the object
at each vertex in its mesh. By matching the learned net force to an estimate of net force based
on finite differences of position, this model is able to find forces that accurately describe the
movement of the object, while resolving issues like mesh interpenetration and lack of contact.
Evaluating on the ContactPose dataset, we show this model successfully corrects poses and finds
contact maps that better match the ground truth, despite not using any RGB or depth image data.
FIRM Project

Abstract:
In a vastly increasing energy dependent society, there is need for the transportation of
energy quickly and efficiently. Laser power beaming and transferring energy with fiber optic
cables are two methods that accomplish this. The low efficiency of converting laser light energy
to electrical energy via photovoltaic cells is a problem. This project aims to optimize the efficiency
of a PV cell for laser light using a theoretical approach. Shockley and Queisserâ€™s methods of a
optimizing a photovoltaic cell for sunlight were used, and the blackbody spectrum of the sun was
replaced with a laser spectrum. The mathematical calculations were done for this laserPV cell
system given the condition that a photon with an energy above the band gap of the PV cell may
create an exciton with energy equivalent to the band gap. By considering the currentvoltage
characteristics of the cell, the practical efficiency equation was found as a function of the laser
frequency, the band gap frequency, and 5 other independent variables. It was used to calculate the
efficiencies of over 600 laserPV cell combinations and to create a color map representing the
three dimensional efficiency function. Most efficiencies were low, however, when the laser
frequency was only slightly over the band gap frequency, practical efficiencies of over 80% were
reached. These findings can be the start of experimentation using the lasercell combinations
highlighted in the paper and can result in a new era of energy transfer.
Advisor: Dr. Brian Monson

