Papers
21
Total Citations
476
H-Index
12
About
Giovanni Iacca is a computational intelligence researcher whose work spans evolutionary computation, swarm intelligence, and embodied artificial intelligence. He is perhaps best known for his pioneering contributions to **compact optimization** — a family of memory-efficient metaheuristic algorithms designed for resource-constrained environments. His 2011 paper on Disturbed Exploitation compact Differential Evolution (129 citations) established him as a leading voice in this niche, with follow-up work on Compact Differential Evolution Light and memory-saving memetic computing for mobile robots further cementing his expertise in lightweight, high-performance optimization. Iacca has also made significant contributions to population-based black-box optimization, introducing novel frameworks such as Multi-Strategy Coevolving Aging Particles (67 citations) and the Re-sampled Inheritance Search algorithm. His more recent work takes a fascinating turn toward evolutionary robotics, where he explores the co-evolution of morphology, sensory systems, and Hebbian learning rules in voxel-based soft robots — bridging classical optimization with bio-inspired adaptive systems. He has additionally contributed to distributed computing through the SPACE framework for peer-and-cloud evolutionary algorithms. Across his career, Iacca exemplifies how algorithmic ingenuity can unlock intelligent behavior even under severe computational constraints.
Research Focus
Key Achievements
Top Papers
- 1
- 2MULTI-STRATEGY COEVOLVING AGING PARTICLE OPTIMIZATION67 citations · 2013
- 3Re-sampled inheritance search: high performance despite the simplicity38 citations · 2013
- 4Memory-saving memetic computing for path-following mobile robots36 citations · 2012
- 5
- 6
- 7Compact Optimization21 citations · 2012
- 8The Seamless Peer and Cloud Evolution Framework16 citations · 2016
- 9
- 10Evolving Hebbian Learning Rules in Voxel-Based Soft Robots14 citations · 2022