Recent Advancements in Evolutionary Multi-Criterion Optimization and Decision-makingSpeaker: Kalyanmoy Deb – MI, United States
Topic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing , Software Engineering and Programming , Computational Theory, Algorithms and Mathematics , Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science , Applied Computing
Evolutionary multi-criterion optimization (EMO) research is now more than three decades old. Efficient algorithms and demonstrative applications have encouraged researchers and practitioners to use these methods in their works, resulting in many public-domain codes and commercial softwares. Due to their working with a population of solutions in each iteration, EMO methods can be applied to solve other problems which are not multi-objective in nature, thereby allowing a flexible and alternate solution approach to many practical problems. Basic EMO methods are extended to address various different problem-solving tasks, such as, constrained problems, dynamic optimization problems, hierarchical problems, large-scale problems, many-objective problems with 5-15 objectives, mixed-integer programming problems, etc. Recently, interactive EMO methods are proposed to address multi-objective optimization and multi-criterion decision-making problems together. EMO obtained solutions are mined to extract variable patterns stored in them, making such extracted information vital knowledge for solving the problem. In this talk, a number of key advanced topics of EMO research and application will be presented with practical case studies.
About this LectureNumber of Slides: 50
Duration: 50 minutes
Languages Available: English
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