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About

Ernesto Costa is a leading researcher in the intersection of evolutionary computation and robotics, with a primary focus on bio-inspired odour source localisation. His work addresses the complex real-world challenge of enabling mobile robots to detect, track, and locate odour sources—a problem with applications in environmental monitoring, search-and-rescue, and hazardous material detection. Costa’s major contributions include pioneering the use of Genetic Programming (GP) to evolve robot controllers for plume tracking, demonstrating how artificial evolution can create adaptive, robust behaviours without explicit programming. His highly cited 2019 comparative study (36 citations) systematically analyses bio-inspired strategies from a state-action perspective, providing a foundational framework for the field. Costa has also advanced the state of the art by introducing Geometric Syntactic Genetic Programming for odour localisation and evolving neural network controllers for multi-robot systems. His work on evolving Infotaxis for meandering environments further extends cognitive search strategies. Through these contributions, Costa has established himself as a key figure in evolutionary robotics, showing how nature-inspired algorithms can solve challenging, dynamic real-world problems.