Mike Preuss is Assistant Professor at LIACS, the computer science institute of Universiteit Leiden in the Netherlands. Previously, he was with the information systems institute of WWU Muenster, Germany (headquarter of ERCIS), and before with the Chair of Algorithm Engineering at TU Dortmund, Germany, where he received his PhD in 2013. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective optimization, and on computational intelligence methods for computer games, and their successful application to real-world problems as chemical retrosynthesis.Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from the University of Otago, Dunedin, New Zealand, respectively. Currently, he is a full professor in the School of Science (Computer Science and Software Engineering) of RMIT University, Melbourne, Australia.  His research interests include evolutionary computation, machine learning, data analytics, multiobjective optimization, dynamic optimization, multimodal optimization, large-scale optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and the International Journal of Swarm Intelligence Research. He is a founding member of the IEEE CIS Task Force on Swarm Intelligence, and a former Chair of the IEEE CIS Task Force on Large-Scale Global Optimization. He is currently a Vice-chair of the IEEE CIS Task Force on Multi-Modal Optimization. He is the recipient of the 2013 SIGEVO Impact Award and the 2017 IEEE CIS "IEEE Transactions on Evolutionary Computation Outstanding Paper Award".Michael G. Epitropakis received his B.S., M.Sc., and Ph.D. degrees from the Department of Mathematics, University of Patras, Patras, Greece. Currently, he is a director of technical products in The Signal Group, Athens, Greece. Previously,he was an Assistant Professor in Data Science at Lancaster University, Lancaster, UK. His current research interests include operations research, computational intelligence, evolutionary computation, swarm intelligence, multi-modal optimization, machine learning, and search-based software engineering. He is a founding member of the IEEE CIS Task Force on Multi-Modal Optimization acting as Chair/Co-Chair from its foundation.Jonathan E. Fieldsend is Professor of Computational Intelligence at the University of Exeter. He has a BA degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has over 100 peer-reviewed publications in the evolutionary computation and machine learning domains, and on the interface between the two. He is a vice-chair of the IEEE Computational Intelligence Society (CIS) Task Forces on Multi-Modal Optimization, and on Data-Driven Evolutionary Optimization of Expensive Problems. He also sits on the IEEE CIS Task Force on Evolutionary Many-Objective Optimization. He is a member of the IEEE Computational Intelligence Society and the ACM SIGEVO.

• Introduction.- Theoretical Studies and Analysis of Niching Methods.- Parameter Adaptation in Niching Methods.- Lowering Computational Cost.- Scalability.- Performance Metrics.- Comparative Studies.- Methods for Machine Learning and Clustering.- Real-World Applications.

Mike Preuss is Assistant Professor at LIACS, the computer science institute of Universiteit Leiden in the Netherlands. Previously, he was with the information systems institute of WWU Muenster, Germany (headquarter of ERCIS), and before with the Chair of Algorithm Engineering at TU Dortmund, Germany, where he received his PhD in 2013. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective optimization, and on computational intelligence methods for computer games, and their successful application to real-world problems as chemical retrosynthesis.Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from the University of Otago, Dunedin, New Zealand, respectively. Currently, he is a full professor in the School of Science (Computer Science and Software Engineering) of RMIT University, Melbourne, Australia.  His research interests include evolutionary computation, machine learning, data analytics, multiobjective optimization, dynamic optimization, multimodal optimization, large-scale optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and the International Journal of Swarm Intelligence Research. He is a founding member of the IEEE CIS Task Force on Swarm Intelligence, and a former Chair of the IEEE CIS Task Force on Large-Scale Global Optimization. He is currently a Vice-chair of the IEEE CIS Task Force on Multi-Modal Optimization. He is the recipient of the 2013 SIGEVO Impact Award and the 2017 IEEE CIS "IEEE Transactions on Evolutionary Computation Outstanding Paper Award".Michael G. Epitropakis received his B.S., M.Sc., and Ph.D. degrees from the Department of Mathematics, University of Patras, Patras, Greece. Currently, he is a director of technical products in The Signal Group, Athens, Greece. Previously,he was an Assistant Professor in Data Science at Lancaster University, Lancaster, UK. His current research interests include operations research, computational intelligence, evolutionary computation, swarm intelligence, multi-modal optimization, machine learning, and search-based software engineering. He is a founding member of the IEEE CIS Task Force on Multi-Modal Optimization acting as Chair/Co-Chair from its foundation.Jonathan E. Fieldsend is Professor of Computational Intelligence at the University of Exeter. He has a BA degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has over 100 peer-reviewed publications in the evolutionary computation and machine learning domains, and on the interface between the two. He is a vice-chair of the IEEE Computational Intelligence Society (CIS) Task Forces on Multi-Modal Optimization, and on Data-Driven Evolutionary Optimization of Expensive Problems. He also sits on the IEEE CIS Task Force on Evolutionary Many-Objective Optimization. He is a member of the IEEE Computational Intelligence Society and the ACM SIGEVO.

• Introduction.- Theoretical Studies and Analysis of Niching Methods.- Parameter Adaptation in Niching Methods.- Lowering Computational Cost.- Scalability.- Performance Metrics.- Comparative Studies.- Methods for Machine Learning and Clustering.- Real-World Applications.

Mike Preuss is Assistant Professor at LIACS, the computer science institute of Universiteit Leiden in the Netherlands. Previously, he was with the information systems institute of WWU Muenster, Germany (headquarter of ERCIS), and before with the Chair of Algorithm Engineering at TU Dortmund, Germany, where he received his PhD in 2013. His research interests focus on the field of evolutionary algorithms for real-valued problems, namely on multimodal and multiobjective optimization, and on computational intelligence methods for computer games, and their successful application to real-world problems as chemical retrosynthesis.Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from the University of Otago, Dunedin, New Zealand, respectively. Currently, he is a full professor in the School of Science (Computer Science and Software Engineering) of RMIT University, Melbourne, Australia.  His research interests include evolutionary computation, machine learning, data analytics, multiobjective optimization, dynamic optimization, multimodal optimization, large-scale optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and the International Journal of Swarm Intelligence Research. He is a founding member of the IEEE CIS Task Force on Swarm Intelligence, and a former Chair of the IEEE CIS Task Force on Large-Scale Global Optimization. He is currently a Vice-chair of the IEEE CIS Task Force on Multi-Modal Optimization. He is the recipient of the 2013 SIGEVO Impact Award and the 2017 IEEE CIS "IEEE Transactions on Evolutionary Computation Outstanding Paper Award".Michael G. Epitropakis received his B.S., M.Sc., and Ph.D. degrees from the Department of Mathematics, University of Patras, Patras, Greece. Currently, he is a director of technical products in The Signal Group, Athens, Greece. Previously,he was an Assistant Professor in Data Science at Lancaster University, Lancaster, UK. His current research interests include operations research, computational intelligence, evolutionary computation, swarm intelligence, multi-modal optimization, machine learning, and search-based software engineering. He is a founding member of the IEEE CIS Task Force on Multi-Modal Optimization acting as Chair/Co-Chair from its foundation.Jonathan E. Fieldsend is Professor of Computational Intelligence at the University of Exeter. He has a BA degree in Economics from Durham University, a Masters in Computational Intelligence from the University of Plymouth and a PhD in Computer Science from the University of Exeter. He has over 100 peer-reviewed publications in the evolutionary computation and machine learning domains, and on the interface between the two. He is a vice-chair of the IEEE Computational Intelligence Society (CIS) Task Forces on Multi-Modal Optimization, and on Data-Driven Evolutionary Optimization of Expensive Problems. He also sits on the IEEE CIS Task Force on Evolutionary Many-Objective Optimization. He is a member of the IEEE Computational Intelligence Society and the ACM SIGEVO.

• Introduction.- Theoretical Studies and Analysis of Niching Methods.- Parameter Adaptation in Niching Methods.- Lowering Computational Cost.- Scalability.- Performance Metrics.- Comparative Studies.- Methods for Machine Learning and Clustering.- Real-World Applications.