Dirk Arnold

Dirk Arnold is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. His research focuses on evolutionary computation, optimization, and surrogate modeling, with applications in machine learning and digital media. He is actively involved in teaching and research, contributing to major conferences and journals.

• PhD, University of Dortmund (2001)
• MSc, Simon Fraser University (1997)
• Diplom, University of Dortmund (1995)

His research interests lie in evolutionary algorithms, particularly evolution strategies, and their application to noisy and constrained optimization. He investigates constraint handling, surrogate modeling, and parameter adaptation mechanisms. His work bridges theoretical analysis and practical applications in image processing, tone mapping, and robotics.

Recent publications show a strong trend toward surrogate-assisted optimization, constrained evolutionary algorithms, and applications in computer vision and graphics. His work is consistently published in top venues such as GECCO, PPSN, and IEEE Transactions on Evolutionary Computation.

Scientific Awards:

• Best Paper Award, Continuous Optimization Track, GECCO 2015
• Best Paper Award, ES/EP Track, GECCO 2013
• Best Paper Award, PPSN XII 2012
• Best Paper Award, ES/EP Track, GECCO 2010
• Best Paper Award, PPSN IX 2006
• Best Student Paper Award, ACM Symposium on Document Engineering 2014

Arnold has advised numerous students, many of whom are co-authors on his publications. He has received research grants supporting work in evolutionary computation and optimization, though specific grant details are not listed. He is a key member of research clusters in Human-Computer Interaction, Visualization & Graphics, and Algorithms & Bioinformatics.

He leads a research group focused on evolutionary algorithms and optimization, collaborating with researchers such as H.-G. Beyer, N. Hansen, and S. Brooks. Future work includes advancing constrained and mixed-integer evolutionary optimization, improving surrogate models, and expanding applications in computer vision and HCI.