The workshop attempts to bring together researchers from evolutionary computation and related areas who are interested in Multimodal Optimization. This is a currently forming field, and we aim for a highly interactive and productive meeting that makes a step forward towards defining it. The Workshop will provide a unique opportunity to review the advances in the current state-of-the-art in the field of Niching methods. Further discussion will deal with several experimental/theoretical scenarios, performance measures, real-world and benchmark problem sets and outline the possible future developments in this area. Positional statements, suggestions, and comments are very welcome!
Population-based meta-heuristic search algorithms such as Evolutionary Algorithms (EAs) or Swarm Intelligence (SI) approaches, in their original forms are usually designed for locating a single global solution. Nevertheless, many real-world problems are multimodal by nature, i.e., multiple satisfactory solutions exist. It may be desirable to locate many such satisfactory solutions so that a decision maker can choose one that is most appropriate for his/her problem domain. Numerous techniques have been developed in the past for locating multiple optima (global or local), and most of these are commonly referred to as niching methods, e.g., crowding, fitness sharing, derating, restricted tournament selection, clearing, speciation.
Multimodal Optimization is currently undergoing a resurgence, re-establishing itself as an active research area that transforms and extends beyond the classic niching methods. This workshop attempts to bring together researchers from many subfields of operational research, evolutionary computation and related areas who are interested in Multimodal Optimization. The Workshop will provide a unique opportunity to discuss the advances in the current state-of-the-art in the field of Niching methods. We intend to hold the workshop in a very interactive fashion, emphasizing discussion, exchange and possibly synthesis of opinions. Prospective topics will include, but are not limited to:
We invite authors/participants to submit new ideas, positional statements, and reviews/summaries/comments on existing work in the field of Multimodal Optimization in the form of extended abstracts (max 2 pages). The workshop will take place at PPSN 2018 in September 2018. Researchers working on finding multiple solutions of multimodal optimization problems are strongly encouraged to contribute!
This workshop is supported by the IEEE CIS Task Force on Multi-modal Optimization.
ERCIS, WWU Muenster,
48149 Münster, Germany
Data Science Institute,
Department of Management Science,
Lancaster University Management School,
Lancaster LA1 4YX, United Kingdom.