Julian Blank

Julian Blank
Michigan State University | MSU · Department of Computer Science and Engineering

PhD Candidate

About

26
Publications
23,628
Reads
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472
Citations
Introduction
Julian Blank currently works at the Department of Computer Science and Engineering , Michigan State University. Julian does research in Surrogate-assisted Multi-objective Optimization.
Additional affiliations
January 2019 - May 2019
Michigan State University
Position
  • Teaching Assistant for Computer Organization and Architecture
October 2014 - January 2015
Otto-von-Guericke-Universität Magdeburg
Position
  • Teaching Assistant for Data Warehouse Technologies
June 2011 - June 2013
Otto-von-Guericke-Universität Magdeburg
Position
  • Research Assistant

Publications

Publications (26)
Preprint
Full-text available
Electric machine design optimization is a computationally expensive multi-objective optimization problem. While the objectives require time-consuming finite element analysis, optimization constraints can often be based on mathematical expressions, such as geometric constraints. This article investigates this optimization problem of mixed computatio...
Article
Full-text available
Most real-world optimization problems consist of multiple objectives to be optimized and multiple constraints to be satisfied. Moreover, the performance assessment of the objective and constraints often requires running different software packages separately along with evaluating mathematically defined functions with significantly different (hetero...
Preprint
Full-text available
Significant effort has been made to solve computationally expensive optimization problems in the past two decades, and various optimization methods incorporating surrogates into optimization have been proposed. However, most optimization toolboxes do not consist of ready-to-run algorithms for computationally expensive problems, especially in combin...
Preprint
Significant effort has been made to solve computationally expensive optimization problems in the past two decades, and various optimization methods incorporating surrogates into optimization have been proposed. Most research focuses on either exploiting the surrogate by defining a utility optimization problem or customizing an existing optimization...
Article
Full-text available
The underlying infrastructure paradigms behind the novel usage scenarios and services are becoming increasingly complex—from everyday life in smart cities to industrial environments. Both the number of devices involved and their heterogeneity make the allocation of software components quite challenging. Despite the enormous flexibility enabled by c...
Article
Full-text available
In this paper, we propose a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: traveling salesman problem and knapsack problem. We address the BI-TTP, a bi-objective version of the TTP, where the goal is to minimize the over...
Chapter
Full-text available
In the past years, a significant amount of research has been done in optimizing computationally expensive and time-consuming objective functions using various surrogate modeling approaches. Constraints have often been neglected or assumed to be a by-product of the expensive objective computation and thereby being available after executing the expen...
Article
Full-text available
Many engineering design problems are associated with computationally expensive and time-consuming simulations for design evaluation. In such problems, each candidate design should be selected carefully, even though it means extra algorithmic complexity. This study develops the Proximity-based Surrogate-Assisted Evolutionary Algorithm (PSA-EA) that...
Conference Paper
Sustainable forest management is a crucial element in combating climate change, plastic pollution, and other unsolved challenges of the 21st century. Forests not only produce wood - a renewable resource that is increasingly replacing fossil-based materials - but also preserve biodiversity and store massive amounts of carbon. Thus, a truly optimal f...
Article
Full-text available
Python has become the programming language of choice for research and industry projects related to data science, machine learning, and deep learning. Since optimization is an inherent part of these research fields, more optimization related frameworks have arisen in the past few years. Only a few of them support optimization of multiple conflicting...
Preprint
Full-text available
In this paper, we propose a method to solve a bi-objective variant of the well-studied Traveling Thief Problem (TTP). The TTP is a multi-component problem that combines two classic combinatorial problems: Traveling Salesman Problem (TSP) and Knapsack Problem (KP). In the TTP, a thief has to visit each city exactly once and can pick items throughout...
Preprint
Full-text available
Python has become the programming language of choice for research and industry projects related to data science, machine learning, and deep learning. Since optimization is an inherent part of these research fields, more optimization related frameworks have arisen in the past few years. Only a few of them support optimization of multiple conflicting...
Article
Most evolutionary many-objective optimization (EMaO) algorithms start with a description of a number of predefined set of reference points on a unit simplex. So far, most studies have used the Das and Dennis’s structured approach for generating well-spaced reference points. Due to the highly structured nature of the procedure, this method cannot pr...
Chapter
Full-text available
Most practical optimization problems are multi-objective in nature. Moreover, the objective values are, in general, differently scaled. In order to obtain uniformly distributed set of Pareto-optimal points, the objectives must be normalized so that any distance metric computation in the objective space is meaningful. Thus, normalization becomes a c...
Chapter
Full-text available
Inmanypracticalmulti-objectiveoptimizationproblems,eval- uation of objectives and constraints are computationally time-consuming, because they require expensive simulation of complicated models. Re- searchers often use a comparatively less time-consuming surrogate or metamodel (model of models) to drive the optimization task. Effective- ness of the...
Conference Paper
Full-text available
The recent advances in evolutionary many-objectiveoptimization (EMOs) have allowed for efficient ways of findinga number of diverse trade-off solutions in three to 15-objectiveproblems. However, there are at least two reasons why the usersare, in some occasions, interested in finding a part, insteadof the entire Pareto-optimal front. First, after a...
Conference Paper
Full-text available
For the last couple of years, the development of many-objective optimization problems opened new avenues of research in the evolutionary multi-objective optimization domain. There are already a number of algorithms to solve such problems, now the next challenge is to interpret the results produced by those algorithms. In this paper, we propose an a...
Conference Paper
In many practical multi-objective optimization problems, evaluations of objectives and constraints are computationally time-consuming because they require expensive simulations of complicated models. In this paper, we propose a metamodel-based multi-objective evolutionary algorithm to make a balance between error uncertainty and progress. In contra...
Conference Paper
Full-text available
This publication investigates characteristics of and algorithms for the quite new and complex Bi-Objective Traveling Thief Problem, where the well-known Traveling Salesman Problem and Binary Knapsack Problem interact. The interdependence of these two components builds an interwoven system where solving one subproblem separately does not solve the o...

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Projects

Projects (2)
Project
In practice it is useful to have a good codebase and well-benchmarked algorithms. Our framework pymoo provides state of the art algorithms and is highly customizable.
Project
Real-world problems tend to have very expensive function evaluations. Therefore, surrogate models are used for cheap low-fidelity evaluations in order to minimize the number of high-fidelity calls.