Erik D. Goodman
Papers
10
Total Citations
550
H-Index
8
About
Erik D. Goodman is a pioneering figure in evolutionary computation and robotics, whose research spans constrained multi-objective optimization, bipedal and quadrupedal locomotion, and robotic spray coating simulation. His most impactful contribution is the improved epsilon constraint-handling method within MOEA/D, a technique that revolutionized the solving of constrained multi-objective optimization problems (CMOPs) with large infeasible regions—a paper that has garnered over 415 citations and remains a cornerstone in the field. Goodman’s work extends to adaptive walking control for biped robots using neural oscillators, and he developed SPRAYTOOL, an accurate simulator for robotic spray application that parameterizes spray patterns with arbitrary precision. His research also includes genetic programming-based gait generation for quadruped robots and synthesis of Matsuoka-based neuron oscillator models for locomotion control. Beyond technical contributions, Goodman is dedicated to education, having pioneered hands-on paradigms for electroactive polymer (EAP) education and led government-industry-university collaborations, such as designing a PSoC controller for a NASA robotic arm. With over 550 total citations, his work continues to influence evolutionary algorithms, robotics, and interdisciplinary engineering education.
Research Focus
Key Achievements
Top Papers
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- 8Analysis and multi-objective optimization of a kind of teaching manipulator10 citations · 2019
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