Frank Neumann serves as Professor and leader of the Optimisation and Logistics group at the University of Adelaide, where he has established himself as a pioneering researcher in evolutionary computation and AI-based optimization. His position is funded by the Australian Research Council through a Future Fellowship, focusing on optimization methods for problems with stochastic constraints. As an internationally recognized scholar with over 11,000 citations, his research spans theoretical aspects of evolutionary computation and practical applications in cybersecurity, renewable energy, logistics, and mining. His leadership in the field includes serving as general chair of ACM GECCO 2016 and co-organizing ACM FOGA 2013 in Adelaide, while maintaining roles as Associate Editor for "Evolutionary Computation" and ACM Transactions on Evolutionary Learning and Optimization. His approach to optimization problems emphasizes diversity in solution sets, developing innovative algorithms that can produce multiple high-quality solutions rather than single outcomes. Through his work at Adelaide's School of Computer Science, he continues to advance the field of evolutionary computation while supervising PhD projects in AI-based optimization, though notably taking a strong stance against "novel" metaphor-based algorithms that he considers detrimental to scientific progress.

Vahid Roostapour completed his PhD in Computer Science at the University of Adelaide under the supervision of Professor Frank Neumann, focusing on bio-inspired computing for complex and dynamic constrained problems. His doctoral research made significant contributions to understanding evolutionary algorithms and ant colony optimization, particularly in addressing problems with dynamically changing constraints. His thesis work spans both static and dynamic combinatorial problems, including innovative research on minimum spanning tree problems, packing while travelling problems, and dynamic knapsack problems. Through his research, he demonstrated the advantages of evolutionary algorithms over traditional approaches in solving complex optimization problems, particularly in environments with high-frequency changes. His work has earned recognition in the field of evolutionary computation, with important contributions to theoretical and empirical understanding of bio-inspired algorithms. Beyond his research, he has been involved in teaching within the Mining Big Data course at Adelaide, combining his expertise in optimization with practical applications in data science.

Aneta Neumann serves as a Researcher at the University of Adelaide's School of Computer Science, where she has established herself as an expert in bio-inspired computation and evolutionary algorithms. Her research spans artificial intelligence, machine learning, and evolutionary computation with a focus on dynamic and stochastic multi-objective optimization for real-world problems in mining, renewable energy, defense, cybersecurity, and public health. She co-chairs the Genetic Algorithms track at GECCO 2024-23 and previously served as co-chair of the Real-World Applications track at GECCO 2022-21. Her groundbreaking work in evolutionary image transition and animation has bridged computational creativity with artificial intelligence, leading to exhibitions of her mixed media artworks inspired by her research. Beyond her primary research, she contributes to education as a co-designer and co-lecturer for the Big Data Fundamentals MOOC in the Big Data MicroMasters® program. Her innovative work in human-interactive EEG-based evolutionary image animation and quality diversity optimization has earned her multiple accolades, including the Hans-Juergen and Marianna Ohff Research Grant and an ACM-W scholarship sponsored by major tech companies.

Wanru (Kelly) Gao serves as a Lecturer in the School of Computer Science at the University of Adelaide, where she has established herself as an expert in evolutionary algorithms and combinatorial optimization. Her research focuses on evolutionary diversity optimization, theoretical analysis of heuristic search methods, and runtime analysis of evolutionary algorithms. Her work bridges theoretical foundations with practical applications in combinatorial optimization problems, contributing significantly to understanding how evolutionary algorithms can be used to generate diverse sets of high-quality solutions. As an educator, she teaches both introductory computer science courses and advanced topics in algorithms and evolutionary computation. Her research has been presented at major conferences in the field, including the Genetic and Evolutionary Computation Conference (GECCO), where she has contributed to advancing the understanding of evolutionary diversity optimization for combinatorial problems. Through her work at Adelaide's School of Computer Science, she continues to explore novel approaches to diversity maximization in evolutionary algorithms while maintaining her commitment to computer science education.