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About

Ed Keedwell is a researcher whose work sits at the intersection of evolutionary computation and autonomous robotics, with a particular focus on applying Gene Expression Programming (GEP) to the automatic design of robotic controllers. His research addresses one of the central challenges in evolutionary robotics: developing robust, scalable methods for encoding and evolving complex robot behaviours without relying on manual programming. Keedwell has made meaningful contributions to the field by demonstrating GEP's viability as a tool for evolving robotic behaviour modules, neuro-controllers, and multi-output controllers — areas where traditional evolutionary approaches have historically struggled. His 2010 paper introducing GEP to evolutionary robotics laid important groundwork, attracting 8 citations, while his subsequent work on sub-behaviour modularity and neuro-controller evolution extended these ideas in increasingly sophisticated directions. Notably, he developed Evolved Linker GEP (EL-GEP), a novel variant of the technique that advances symbolic regression capabilities beyond standard GEP frameworks. Across his cited publications, Keedwell has consistently pushed toward more modular, expressive representations for evolved robot intelligence — work that remains relevant as the field continues seeking automated, efficient pathways to capable autonomous systems.