Enrique Naredo

Papers

Total Citations

H-Index

About

Enrique Naredo is a researcher whose work lies at the intersection of evolutionary computation and machine learning, with a particular focus on novelty search and genetic programming. His key contributions center on rethinking how search algorithms explore problem spaces, moving beyond traditional fitness-based optimization toward methods that prioritize behavioral novelty. In his most cited work, "Searching for novel regression functions" (2013, 22 citations), Naredo demonstrated how novelty search can discover diverse and unexpected solutions in regression tasks, challenging the conventional reliance on explicit objective functions. He extended this paradigm to classification problems in "Searching for Novel Classifiers" (2013, 17 citations) and "Evolving genetic programming classifiers with novelty search" (2016, 12 citations), showing that novelty-driven evolution can yield classifiers with surprising and effective behaviors. Though his citation counts are modest, Naredo’s work is notable for its conceptual boldness, offering a fresh perspective on how to escape local optima and foster creativity in automated problem-solving. His research is particularly valuable for students and researchers interested in alternative search strategies, open-ended evolution, and the intersection of artificial intelligence and exploratory algorithms.