Max Robotics — Global Service Robot Directory + US Certification

About

Ye-Hoon Kim’s research lies at the intersection of evolutionary multiobjective optimization and autonomous mobile robotics, with a strong emphasis on practical, real-world applications. His most influential work, “Preference-Based Solution Selection Algorithm for Evolutionary Multiobjective Optimization” (101 citations), addresses a critical gap in multiobjective evolutionary algorithms (MOEAs) by enabling decision-makers to efficiently select preferred solutions from a set of nondominated alternatives—a key step for deploying these algorithms in engineering contexts. Kim has also made significant contributions to robot navigation and education. His 2009 paper on evolutionary multiobjective optimization in robot soccer systems (67 citations) demonstrates how computational intelligence can be taught through competitive, hands-on platforms. More recently, his 2018 work on end-to-end deep learning for autonomous navigation (53 citations) proposes a streamlined convolutional neural network approach that bypasses traditional multi-step pipelines, directly mapping camera inputs to control commands. Across his career, Kim has explored diverse robotic platforms, from fuzzy path planning for mobile robots (31 citations) to multiobjective footstep planning for humanoid robots (23 citations). His work consistently bridges theoretical optimization algorithms with tangible robotic systems, making him a notable figure in computational intelligence and robotics education.