Tutorial on Evolutionary Bilevel Optimization at CEC 2015

Congress on Evolutionary Computation (CEC) 2015
May 25-28 2015, Sendai, Japan

EBO Tutorial Slides (To be available soon)

Who would like to attend?

Anyone oriented for practice or academic research and is interested in evolutionary optimization and their applications. No prerequisite or prior knowledge is needed to attend this tutorial.


Bilevel optimization problems involve two levels of optimization. A solution at the upper level may be considered feasible only if it is optimal for a parametric lower level optimization problem. Such optimization problems are commonly found in transportation, engineering design, game playing and business models. They are also known as Stackelberg games in the operations research community. These problems are too complex to be solved using classical optimization methods simply due to the "nestedness" of one optimization task into another.

Evolutionary Algorithms (EAs) provide some amenable ways to solve such problems due to their flexibility and ability to handle constrained search spaces efficiently. In the recent past, there has been a surge in research activities towards solving bilevel optimization problems. In this tutorial, we will introduce principles of bilevel optimization for single and multiple objectives, and discuss the difficulties in solving such problems in general. With a brief survey of the existing literature, we will present a few viable evolutionary algorithms for both single and multi-objective EAs for bilevel optimization. Our recent studies on bilevel test problems and some application studies will be discussed. Finally, a number of immediate and future research ideas on bilevel optimization will also be highlighted.


Dr. Ankur Sinha
Indian Institute of Management, Ahmedabad, India

Prof. Kalyanmoy Deb
Michigan State University, East Lansing, MI, USA


Bilevel Optimization, Bilevel Multi-objective Optimization, Evolutionary Algorithms, Multi-Criteria Decision Making, Theory on Bilevel Programming, Hierarchical Decision Making, Bilevel Applications, Hybrid Algorithms.