Papers
25
Total Citations
323
H-Index
10
About
Hitoshi Iba is a pioneering researcher at the forefront of evolutionary computation and multi-agent robotics. His work masterfully bridges genetic programming (GP) and reinforcement learning (RL), enabling real robots to adapt and cooperate in dynamic, unpredictable environments without relying on precise simulators. His seminal 1996 paper on emergent cooperation for multiple agents using GP (62 citations) laid the groundwork for a rich body of research on collaborative behavior, including the pursuit game and cooperative transportation with humanoid robots. Iba’s integrated GP-RL technique, detailed in his highly cited 2005 work (48 citations), allows real robots to learn positioning and correct errors during joint tasks—a critical step toward practical multi-robot systems. His contributions extend to theory as well, with his 2018 book on evolutionary approaches to machine learning and deep neural networks (20 citations) and his 2011 volume on new frontiers in evolutionary algorithms (12 citations) serving as key resources for students and researchers. With over 250 total citations across his most impactful works, Iba’s research continues to inspire advances in adaptive, cooperative artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1Emergent cooperation for multiple agents using genetic programming62 citations · 1996
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- 4Evolutionary Approach to Machine Learning and Deep Neural Networks20 citations · 2018
- 5Evolving communicating agents based on genetic programming20 citations · 2002
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- 9Random sampling algorithm for multi-agent cooperation planning13 citations · 2005
- 10New Frontier in Evolutionary Algorithms: Theory and Applications12 citations · 2011